2 Graphical Models in a Nutshell (2009)
Daphne Koller, Nir Friedman, Lise Getoor, Ben Taskar
Probabilistic graphical models are an elegant framework which combines uncertainty (probabilities) and logical structure (independence constraints) to compactly represent complex, real-world...
Pages 1–9 Rich Probabilistic Models for Gene Expression (2009)
Eran Segal, Ben Taskar, Audrey Gasch, Nir Friedman, Daphne Koller
Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist...
5 Probabilistic Relational Models (2009)
Lise Getoor, Nir Friedman, Daphne Koller, Avi Pfeffer, Ben Taskar
Probabilistic relational models (PRMs) are a rich representation language for structured statistical models. They combine a frame-based logical representation with probabilistic semantics based on...
Gibbs Sampling in Factorized Continuous-Time Markov Processes (2009)
Tal El-hay, Nir Friedman, Raz Kupferman
A central task in many applications is reasoning about processes that change over continuous time. Continuous-Time Bayesian Networks is a general compact representation language for multi-component...
Convexifying the Bethe Free Energy (2009)
Meshi, Ofer, Jaimovich, Ariel, Globerson, Amir, Friedman, Nir
The introduction of loopy belief propagation (LBP) revitalized the application of graphical models in many domains. Many recent works present improvements on the basic LBP algorithm in an attempt to...
Abstract Learning Probabilistic Relational Models (2009)
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat ” data representations. Thus, to apply...
Identifying novel constrained elements by exploiting biased substitution patterns (2009)
Garber, Manuel, Guttman, Mitchell, Clamp, Michele, Zody, Michael C., Friedman, Nir, Xie, Xiaohui
Motivation: Comparing the genomes from closely related species provides a powerful tool to identify functional elements in a reference genome. Many methods have been developed to identify conserved...
Abstract Learning Probabilistic Relational Models (2008)
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat ” data representations. Thus, to apply...
A Functional and Regulatory Map of Asthma (2008)
Noa Novershtern, Zohar Itzhaki, Ohad Manor, Nir Friedman, Naftali Kaminski
The prevalence and morbidity of asthma, a chronic inflammatory airway disease, is increasing. Animal models provide a meaningful but limited view of the mechanisms of asthma in humans. A systemslevel...
Abstract Learning Probabilistic Relational Models (2008)
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat ” data representations. Thus, to apply...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Itay Mayrose, Nir Friedman, Tal Pupko
A Gamma mixture model better accounts for among site rate heterogeneity
Abstract Learning Probabilistic Relational Models (2008)
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat ” data representations. Thus, to apply...
A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval (2008)
Naomi Habib, Tommy Kaplan, Hanah Margalit, Nir Friedman
Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences...
Nir Friedman, Iftach Nachman, Michal Linial
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of the cell’s transcriptions. A major challenge in...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Nir Friedman, Iftach Nachman, Michal Linial
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in...
In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze...
Discovering Motif Interactions in Gene Promoter Regions (2008)
We seek to understand the rules governing the organization of cis-regulatory elements in promoter regions of saccharomyces cerevisiae(yeast). Starting with a library of known motifs, we construct a...
Abstract Learning Probabilistic Relational Models (2008)
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat ” data representations. Thus, to apply...
ABSTRACT From Promoter Sequence to Expression: A Probabilistic Framework (2008)
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key...
Nir Friedman, Iftach Nachman, Michal Linial
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in...
Itay Mayrose, Nir Friedman, Tal Pupko
A Gamma mixture model better accounts for among site rate heterogeneity
Nir Friedman, Moises Goldszmidt
In recent years, there has been much interest in learning Bayesian networks from data. Learning such models is desirable simply because there is a wide array of off-the-shelf tools that can apply the...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks (2008)
Continuous-time Bayesian networks (CTBNs) are graphical representations of multi-component continuous-time Markov processes as directed graphs. The edges in the network represent direct influences...
Nir Friedman, Iftach Nachman, Michal Linial
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of the cell’s transcriptions. A major challenge in...
BIOINFORMATICS Articles (2008)
Matan Ninio, Eyal Privman, Tal Pupko, Nir Friedman
pairwise distance estimation using Bayesian inference of evolutionary rates Vol. 00 no. 00 2006, pages 1–6 doi:10.1093/bioinformatics/btl304
In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes,most of which do not appear in the training data. We analyze...
Abstract Learning Probabilistic Relational Models (2008)
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat ” data representations. Thus, to apply...
Information And Conditional, Nir Friedman
We propose a new scoring function for learning Bayesian networks from data using score search algorithms. This is based on the concept of mutual information and exploits some well-known properties of...
Mitochondrial processes are impaired in hereditary inclusion body myopathy (2008)
Eisenberg, Iris, Novershtern, Noa, Itzhaki, Zohar, Becker-Cohen, Michal, Sadeh, Menachem, ...
Hereditary inclusion body myopathy (HIBM) is an adult onset, slowly progressive distal and proximal myopathy. Although the causing gene, GNE, encodes for a key enzyme in the biosynthesis of sialic...
Nucleosome positioning from tiling microarray data (2008)
Yassour, Moran, Kaplan, Tommy, Jaimovich, Ariel, Friedman, Nir
Motivation: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory...
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations....
Nir Friedman, Moises Goldszmidt
In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being...
Amir Ben-dor, Nir Friedman, Zohar Yakhini
Recent studies (Alizadeh et al, [1]; Bittner et al,[5]; Golub et al, [11]) demonstrate the discovery of putative disease subtypes from gene expression data. The underlying computational problem is to...
Translational Review Practical Approaches to Analyzing Results of Microarray Experiments (2007)
Naftali Kaminski, Nir Friedman
Microarray technology is rapidly becoming a standard laboratory technique. The main challenges related to the successful implementation of the technology are analysis-related. In this article we...
Eran Segal, Bauer Ctr, Daphne Koller, Nir Friedman
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and...
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction is by maximum likelihood...
Tal Pupko, Masami Hasegawa, Dan Graur, Nir Friedman
A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: Application to the evolution of five gene families
Branch-and-Bound Reconstrucion 1 Branch-and-Bound Reconstruction of Ancestral Sequences (2007)
The problem of ancestral sequence reconstruction is the statistical inference of sequences that correspond to internal nodes in a phylgenetic tree [1]. Joint reconstruction is the task of seeking the...
Pages 1--9 Inferring Subnetworks from Perturbed Expression Profiles (2007)
Aviv Regev, Gal Elidan, Nir Friedman
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by...
Tal Pupko, Masami Hasegawa, Dan Graur, Nir Friedman
A branch-and-bound algorithm for the inference of ancestral
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. In this work, we...
Pages 1--9 Inferring Subnetworks from Perturbed Expression Profiles (2007)
Aviv Regev, Gal Elidan, Nir Friedman
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by...
Pages 1--9 Inferring Subnetworks from Perturbed Expression Profiles (2007)
Aviv Regev, Gal Elidan, Nir Friedman
Genome-wide expression profiles of genetic mutants provide a wide variety of transcripts measuring the response of cells to perturbations. Standard analysis of such data identifies genes that were...
Nir Friedman, Iftach Nachman, Michal Linial
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of the cell’s transcriptions. A major challenge in...
Eran Segal, Ben Taskar, Audrey Gasch, Nir Friedman, Daphne Koller
Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist...
Nir Friedman, Moises Goldszmidt
We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local...
Updated August 29, 1999 (2007)
Bayesian Classification Nir, Nir Friedman, Ron Kohavi
Bayesian classification addresses the classification problem by learning the distribution of instances given different class values. We review the basic notion of Bayesian classification, describe in...
Ninio, Matan, Privman, Eyal, Pupko, Tal, Friedman, Nir
Distance-based methods for phylogeny reconstruction are the fastest and easiest to use, and their popularity is accordingly high. They are also the only known methods that can cope with huge datasets...
Interplay between parallel and diagonal electronic nematic phases in interacting systems (2006)
Doh, Hyeonjin, Friedman, Nir, Kee, Hae-Young
An electronic nematic phase can be classified by a spontaneously broken discrete rotational symmetry of a host lattice. In a square lattice, there are two distinct nematic phases. The parallel...
Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae (2005)
Chih Long Liu, Tommy Kaplan, Minkyu Kim, Stephen Buratowski, Stuart L. Schreiber, Nir Friedman, ...
High-resolution microarrays were used to investigate 12 histone modifications across thousands of yeast nucelosomes in vivo. Two main groups co-occurred, consistent with the redundant histone code...
Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae (2005)
Chih Long Liu, Tommy Kaplan, Minkyu Kim, Stephen Buratowski, Stuart L. Schreiber, Nir Friedman, ...
Covalent modification of histone proteins plays a role in virtually every process on eukaryotic DNA, from transcription to DNA repair. Many different residues can be covalently modified, and it has...
Nir Friedman, Shuki Vardi, Michal Ronen, Uri Alon, Joel Stavans
Oscillations in transcriptional activity in the network responsible for controlling DNA damage are monitored with GFP- promoter fusions in individual E. coli cells.
Nir Friedman, Shuki Vardi, Michal Ronen, Uri Alon, Joel Stavans
The SOS genetic network is responsible for the repair/bypass of DNA damage in bacterial cells. While the initial stages of the response have been well characterized, less is known about the dynamics...
Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge (2005)
Tommy Kaplan, Nir Friedman, Hanah Margalit
Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable...
H (2005) Predicting transcription factor binding sites using structural knowledge (2005)
Tommy Kaplan, Nir Friedman, Hanah Margalit
Abstract. Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach...
ab initio prediction of transcription factor targets using structural knowledge (2005)
Tommy Kaplan, Nir Friedman, Hanah Margalit
Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable...
Single-nucleosome mapping of histone modifications in S. cerevisiae. PLoS Biol (2005)
Chih Long Liu, Tommy Kaplan, Minkyu Kim, Stephen Buratowski, Stuart L. Schreiber, Nir Friedman, ...
Covalent modification of histone proteins plays a role in virtually every process on eukaryotic DNA, from transcription to DNA repair. Many different residues can be covalently modified, and it has...
Learning module networks (2005)
Eran Segal, Daphne Koller, Nir Friedman, Tommi Jaakkola
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and...
Learning hidden variable networks: The information bottleneck approach (2005)
Gal Elidan, Nir Friedman, Maxwell Chickering
A central challenge in learning probabilistic graphical models is dealing with domains that involve hidden variables. The common approach for learning model parameters in such domains is the...
Learning module networks (2005)
Eran Segal, Daphne Koller, Nir Friedman, Tommi Jaakkola
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and...
Towards an integrated protein-protein interaction network (2005)
Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman
Protein–protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has...
Towards an integrated protein-protein interaction network (2005)
Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman
Abstract. Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance,...
Towards an integrated protein-protein interaction network (2005)
Ariel Jaimovich, Gal Elidan, Hanah Margalit, Nir Friedman
Abstract. Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance,...
A Gamma mixture model better accounts for among site rate heterogeneity (2005)
Mayrose, Itay, Friedman, Nir, Pupko, Tal
Motivation: Variation of substitution rates across nucleotide and amino acid sites has long been recognized as a characteristic of molecular sequence evolution. Evolutionary models that account for...
Atom-Optics Billiards: Non-linear dynamics with cold atoms in optical traps (2004)
Kaplan, Ariel, Andersen, Mikkel, Friedman, Nir, Davidson, Nir
We present a new experimental system (the ``atom-optics billiard'') and demonstrate chaotic and regular dynamics of cold, optically trapped atoms. We show that the softness of the walls and...
Computational Aspects in Gene Expression Analysis (2004)
Noa Shefi, Supervised Prof, Nir Friedman
First and foremost, I would like to thank my supervisor prof. Nir Friedman for bringing me into the world of research and showing me the ways of science. I would also like to thank my dear friends in...
The “ideal parent” structure learning for continuous variable networks (2004)
Iftach Nachman, Gal Elidan, Nir Friedman
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits...
The “ideal parent” structure learning for continuous variable networks (2004)
Gal Elidan, Iftach Nachman, Nir Friedman, Maxwell Chickering
Bayesian networks in general, and continuous variable networks in particular, have become increasingly popular in recent years, largely due to advances in methods that facilitate automatic learning...
not be interpreted as representing the official policies, either expressed or implied, of the NSF, the U.S. government or any other entity.
Efficient exact p-value computation for small sample, sparse, and surprising categorical data (2004)
Gill Bejerano, Nir Friedman, Naftali Tishby
A major obstacle in applying various hypothesis testing procedures to datasets in bioinformatics is the computation of ensuing p-values. In this paper, we define a generic branchand-bound approach to...
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays (2004)
Barash, Yoseph, Dehan, Elinor, Krupsky, Meir, Franklin, Wilbur, Geraci, Marc, Friedman, Nir, ...
Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various...
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays (2004)
Barash, Yoseph, Dehan, Elinor, Krupsky, Meir, Franklin, Wilbur, Geraci, Marc, Friedman, Nir, ...
Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various...
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays (2004)
Barash, Yoseph, Dehan, Elinor, Krupsky, Meir, Franklin, Wilbur, Geraci, Marc, Friedman, Nir, ...
Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various...
Modeling Belief in Dynamic Systems, Part I: Foundations (2003)
Friedman, Nir, Halpern, Joseph Y.
Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations...
Modeling Belief in Dynamic Systems, Part II: Revisions and Update (2003)
Friedman, Nir, Halpern, Joseph Y.
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
Modeling dependencies in protein-dna binding sites (2003)
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and...
This work was carried out under the supervision of
This work was carried out under the supervision of
The information bottleneck EM algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
PCluster: Probabilistic Agglomerative Clustering of Gene (2003)
Expression Profiles Nir, Nir Friedman
A central problem in analysis of gene expression data is clustering of genes with similar expression profiles. In this paper, I describe an hierarchical clustering procedure that is based on simple...
Eran Segal, Michael Shapira, Aviv Regev, Dana Pe'er, David Botstein, Daphne Koller, ...
Introduction The complex functions of a living cell are carried out through the concerted activity of many genes and gene products. This activity is often coordinated by the organization of Computer...
Modeling Dependencies in Protein-DNA Binding Sites (2003)
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and...
The information bottleneck EM algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Modeling dependencies in protein-dna binding sites (2003)
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and...
The Information Bottleneck EM Algorithm (2003)
Gal Elidan And, Gal Elidan, Nir Friedman
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Pcluster: Probabilistic agglomerative clustering of gene expression profiles (2003)
A central problem in analysis of gene expression data is clustering of genes with similar expression profiles. In this paper, I describe an hierarchical clustering procedure that is based on simple...
The Information Bottleneck EM Algorithm (2003)
Gal Elidan And, Gal Elidan, Nir Friedman
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Probabilistic models for identifying regulation networks (2003)
Microarray-based hybridization methods techniques allow to simultaneously measure the expression level for thousands of genes. Such measurements contain information about many different aspects of...
Stable regions and singular trajectories in chaotic soft wall billiards (2002)
Kaplan, Ariel, Friedman, Nir, Andersen, Mikkel, Davidson, Nir
We present numerical and experimental results for the development of islands of stability in atom-optics billiards with soft walls. As the walls are soften, stable regions appear near singular...
A structural EM algorithm for phylogenetic inference (2002)
Matan Ninio, B. Sc, Dr. Nir Friedman, Danny Dolev, Matan Ninio
Head of the school for Engineering and Computer Science
Data perturbation for escaping local maxima in learning (2002)
Gal Elidan, Matan Ninio, Nir Friedman
Almost all machine learning algorithms—be they for regression, classification or density estimation—seek hypotheses that optimize a score on training data. In most interesting cases, however,...
Data perturbation for escaping local maxima in learning (2002)
Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans
Almost all machine learning algorithms---be they for regression, classification or density estimation---seek hypotheses that optimize a score on training data. In most interesting cases, however,...
A structural EM algorithm for phylogenetic inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum...
A structural EM algorithm for phylogenetic inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum...
From promoter sequence to expression: A probabilistic framework (2002)
Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Keller
nir @ cs.huji.ac.il koller @ cs.stanford.edu We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint...
A structural EM algorithm for phylogenetic inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum...
From promoter sequence to expression: A probabilistic framework (2002)
Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unies the two key...
A structural EM algorithm for phylogenetic inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction is by maximum likelihood...
A structural EM algorithm for phylogenetic inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction performs maximum likelihood...
From Promoter Sequence to Expression: (2002)
Probabilistic Framework Eran, Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key...
Learning probabilistic models of link structure (2002)
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with...
Learning probabilistic models of link structure (2002)
Lise Getoor, Nir Friedman, Daphne Koller, Ben Taskar
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with...
Learning probabilistic models of link structure (2002)
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with...
Robust Temporal and Spectral Modeling for Query by Melody (2002)
Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer
Query by melody is the problem of retrieving musical performances from melodies. Retrieval of real performances is complicated due to the large number of variations in performing a melody and the...
Learning probabilistic models of link structure (2002)
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with...
Pupko, Tal, Pe'er, Itsik, Hasegawa, Masami, Graur, Dan, Friedman, Nir
Motivation: We developed an algorithm to reconstruct ancestral sequences, taking into account the rate variation among sites of the protein sequences. Our algorithm maximizes the joint probability of...
Belief Revision: A Critique (2001)
Friedman, Nir, Halpern, Joseph Y.
We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change...
Multivariate information bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution¢¤£¦¥¨§�©� � , this method constructs a new variable � that...
Learning probabilistic models of relational structure (2001)
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat " data representations, forcing us to convert our data into a form...
Learning probabilistic models of relational structure (2001)
Lise Getoor, Nir Friedman, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat ” data representations, forcing us to convert our data into a form that loses...
Multivariate information bottleneck (2001)
Noam Slonim, Nir Friedman, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts partitions,...
Multivariate information bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts partitions,...
Multivariate information bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts partitions,...
Multivariate information bottleneck (2001)
Noam Slonim, Nir Friedman, Naftali Tishby
The information bottleneck method is an unsupervised model independent data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts...
A simple hyper-geometric approach for discovering putative transcription factor binding sites (2001)
Yoseph Barash, Gill Bejerano, Nir Friedman
Abstract. A central issue in molecular biology is understanding the regulatory mechanisms that control gene expression. The recent ood of genomic and post-genomic data opens the way for computational...
Context-specific bayesian clustering for gene expression data (2001)
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. In this work, we...
Learning the dimensionality of hidden variables (2001)
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden...
Global variational approximation methods in graphical models allow efficient approximate inference of complex posterior distributions by using a simpler model. The choice of the approximating model...
Multivariate information bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts partitions,...
Learning the dimensionality of hidden variables (2001)
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden...
Context-specific bayesian clustering for gene expression data (2001)
The recent growth in genomic data and measurement of genomewide expression patterns allows to examine gene regulation by transcription factors using computational tools. In this work, we present a...
Global variational approximation methods in graphical models allow efficient approximate inference of complex posterior distributions by using a simpler model. The choice of the approximating model...
Multivariate information bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution ¢¤£¦¥¨§�©� � , this method constructs a new variable � that...
Why Causal Networks? Explanation and Prescription (2001)
Nir Friedman, Michal Linial, Iftach Nachman, Dana Peér, Ruchira Datta
• Discriminant analysis seeks to identify genes which sort the cellular snapshots into previously defined classes. • Cluster analysis seeks to identify genes which vary together, thus identifying...
Learning probabilistic models of relational structure (2001)
Lise Getoor, Nir Friedman, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat ” data representations, forcing us to convert our data into a form that loses...
Multivariate information bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distributionÈ���,this method constructs a new variableÌthat extracts partitions,...
Learning probabilistic models of relational structure (2001)
Lise Getoor, Nir Friedman, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat ” data representations, forcing us to convert our data into a form that loses...
Observation of Islands of Stability in Soft Wall Atom-Optics Billiards (2001)
Andersen, Mikkel, Davidson, Nir, Friedman, Nir, Kaplan, Ariel
Inferring subnetworks from perturbed expression profiles (2001)
Peer, Dana, Regev, Aviv, Elidan, Gal, Friedman, Nir
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by...
Rich probabilistic models for gene expression (2001)
Segal, Eran, Taskar, Ben, Gasch, Audrey, Friedman, Nir, Koller, Daphne
Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist...
Learning the dimensionality of hidden variables (2001)
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden...
Being Bayesian about network structure (2000)
Abstract. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent...
Using Bayesian networks to analyze expression data (2000)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in...
Using Bayesian networks to analyze expression data (2000)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in...
Being Bayesian about network structure (2000)
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model-selection attempts to find the...
Likelihood computations using value abstraction (2000)
Nir Friedman, Dan Geiger, Noam Lotner
In this paper, we use evidence-specific value abstraction for speeding Bayesian networks inference. This is done by grouping variable values and treating the combined values as a single entity. As we...
Being Bayesian about network structure (2000)
Abstract. In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model selection attempts...
Discovering hidden variables: A structure-based approach (2000)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Discovering hidden variables: A structure-based approach (2000)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Being Bayesian about network structure (2000)
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model-selection attempts to find the...
Being Bayesian about network structure (2000)
Abstract. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent...
Discovering hidden variables: A structure-based approach (2000)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Learning Probabilistic Relational Models with Structural Uncertainty (2000)
Lise Getoor, Daphne Koller, Benjamin Taskar, Nir Friedman
Most real-world data is stored in relational form. In contrast, most statistical learning methods, e.g., Bayesian network learning, work only with "flat " data representations,...
Being Bayesian about network structure (2000)
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model-selection attempts to find the...
Discovering hidden variables: A structure-based approach (2000)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Gaussian Process Networks (2000)
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures....
From Instances to Classes in Probabilistic Relational Models (2000)
Lise Getoor, Daphne Koller, Nir Friedman
Probabilistic graphical models, in particular Bayesian networks, are useful models for representing statistical patterns in propositional domains. Recent work develops effective techniques for...
Tissue Classification with Gene Expression Profiles (2000)
Amir Ben-Dor, Laurakay Bruhn, Agilent Laboratories, Nir Friedman, Miche`l Schummer, Iftach Nachman, ...
Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to...
Gaussian Process Networks (2000)
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures....
Using Bayesian Networks to Analyze Expression Data (2000)
Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of the cell's transcriptions. A major challenge in...
Using Bayesian Networks to Analyze Expression Data (2000)
Nir Friedman Hebrew, Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of the cell's transcriptions. A major challenge in...
Tissue Classification with Gene Expression Profiles (2000)
Amir Ben-Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michel Schummer, Zohar Yakhini
Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also A preliminary...
Amir Ben-dor, Nir Friedman, Michèl Schummer, Laurakay Bruhn, Iftach Nachman, Zohar Yakhini
Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also
Being Bayesian about network structure (2000)
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model-selection attempts to find the...
Being Bayesian about network structure (2000)
Abstract. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent...
Likelihood computations using value abstraction (2000)
In this paper, we use evidence-specific value abstraction for speeding Bayesian networks inference. This is done by grouping variable values and treating the combined values as a single entity. As we...
Likelihood computations using value abstraction (2000)
In this paper, we use evidence-specific value abstraction for speeding Bayesian networks inference. This is done by grouping variable values and treating the combined values as a single entity. As we...
Being Bayesian about network structure (2000)
Abstract. In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model selection attempts...
Using Bayesian networks to analyze expression data (2000)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in...
Using Bayesian networks to analyze expression data (2000)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in...
Learning probabilistic relational models (1999)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations....
Learning Bayesian network structure from massive datasets: The “sparse candidate” algorithm (1999)
Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing...
Discovering the hidden structure of complex dynamic systems (1999)
Xavier Boyen, Nir Friedman, Daphne Koller
Dynamic Bayesian networks provide a compact and natural representation for complex dynamic systems. However, in many cases, there is no expert available from whom a model can be elicited. Learning...
Learning Bayesian network structure from massive datasets: The “sparse candidate” algorithm (1999)
Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing...
Efficient bayesian parameter estimation in large discrete domains (1999)
In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze...
Modeling Belief in Dynamic Systems. Part II: Revision and Update (1999)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
Modeling belief in dynamic systems. Part II: revision and update (1999)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
Discovering the Hidden Structure of Complex Dynamic Systems (1999)
Xavier Boyen Computer, Xavier Boyen, Nir Friedman, Daphne Koller
Dynamic Bayesian networks provide a compact and natural representation for complex dynamic systems. However, in many cases, there is no expert available from whom a model can be elicited. Learning...
Model based Bayesian Exploration (1999)
Richard Dearden, Nir Friedman, David Andre
Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...
Efficient Learning using Constrained Sufficient Statistics (1999)
Learning Bayesian networks is a central problem for pattern recognition, density estimation and classification. In this paper, we propose a new method for speeding up the computational process of...
Using Bayesian Networks to Analyze Expression Data (1999)
Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot " of transcription levels within the cell. A major...
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm (1999)
Nir Friedman, Iftach Nachman, Dana Peér
Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing...
Efficient Bayesian Parameter Estimation in Large Discrete Domains (1999)
We examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze this problem...
Structured Representation of Complex Stochastic Systems (1999)
Nir Friedman, Daphne Koller, Avi Pfeffer
This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes....
Plausibility Measures and Default Reasoning: An Overview (1999)
Nir Friedman, Joseph Y. Halpern
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures,...
Discovering the Hidden Structure of Complex Dynamic Systems (1999)
Xavier Boyen, Nir Friedman, Daphne Koller
Dynamic Bayesian networks provide a compact and natural representation for complex dynamic systems. However, in many cases, there is no expert available from whom a model can be elicited. Learning...
Nir Friedman, Iftach Nachman, Dana Peer, Dana Pe Er
Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing...
Learning Probabilistic Relational Models (1999)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to...
Model based Bayesian Exploration (1999)
Richard Dearden, Nir Friedman, David Andre
Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...
Nir Friedman The, Nir Friedman, Moises Goldszmidt
In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of...
Nir Friedman The, Nir Friedman, Moises Goldszmidt
In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of...
Nir Friedman, Moises Goldszmidt
In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of...
Data Analysis with Bayesian Networks: A Bootstrap Approach (1999)
Nir Friedman, Moises Goldszmidt, Abraham Wyner
In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being...
Efficient Learning using Constrained Sufficient Statistics (1999)
Learning Bayesian networks is a central problem for pattern recognition, density estimation and classification. In this paper, we propose a new method for speeding up the computational process of...
Modeling Belief in Dynamic Systems. Part II: Revision and Update (1999)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
Using Bayesian Networks to Analyze Expression Data (1999)
Nir Friedman Hebrew, Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major...
Plausibility Measures and Default Reasoning: An Overview (1999)
Nir Friedman, Joseph Y. Halpern
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures,...
C5.1.5 Bayesian Classi cation (1999)
Bayesian classi cation addresses the classi cation problem by learning the distribution of instances given di erentclassvalues. We review the basic notion of Bayesian classi cation, describe in some...
Data analysis with Bayesian networks: A bootstrap approach (1999)
In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being...
Modeling belief in dynamic systems. Part II: revision and update (1999)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of...
In many domains, we are interested in analyzing the structure of the underlying distribution e.g. whether one variable is a direct parent of the other. Bayesian model-selection attempts to find the...
Plausibility Measures and Default Reasoning (1998)
Friedman, Nir, Halpern, Joseph Y.
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures,...
First-Order Conditional Logic Revisited (1998)
Friedman, Nir, Halpern, Joseph Y., Koller, Daphne
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order...
Learning the structure of dynamic probabilistic networks (1998)
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules...
The Bayesian Structural EM Algorithm (1998)
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from...
Generalized Prioritized Sweeping (1998)
David Andre, Nir Friedman, Ronald Parr
Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment...
Generalized Prioritized Sweeping (1998)
David Andre, Nir Friedman, Ronald Parr
Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment...
Learning the Structure of Dynamic Probabilistic Networks (1998)
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules...
Structured representation of complex stochastic systems (1998)
Nir Friedman, Daphne Koller, Avi Pfeffer
This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes....
Learning the structure of dynamic probabilistic networks (1998)
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules...
Learning the Structure of Dynamic Probabilistic Networks (1998)
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules...
Nir Friedman, Moises Goldszmidt, Thomas J. Lee
In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs...
Belief Revision with Unreliable Observations (1998)
Craig Boutilier, Nir Friedman, J. Halpern, Joseph Y. Halpern
Research in belief revision has been dominated by work that lies firmly within the classic AGM paradigm, characterized by a well-known set of postulates governing the behavior of "rational...
Nir Friedman, Moises Goldszmidt, Thomas J. Lee
In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs...
Belief Revision with Unreliable Observations (1998)
Craig Boutilier, Nir Friedman, Joseph Y. Halpern
Research in belief revision has been dominated by work that lies firmly within the classic AGM paradigm, characterized by a well-known set of postulates governing the behavior of "rational...
Learning the Structure of Dynamic Probabilistic Networks (1998)
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules...
Richard Dearden, Nir Friedman, Stuart Russell
A central problem in learning in complex environments is balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...
Richard Dearden, Nir Friedman, Stuart Russell
A central problem in learning in complex environments is balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...
Structured representation of complex stochastic systems (1998)
This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes....
In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs...
Learning the structure of dynamic probabilistic networks (1998)
Nir Friedman, Kevin Murphy, Stuart Russell
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules...
Generalized prioritized sweeping (1998)
David Andre, Nir Friedman, Ronald Parr
Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent’s limited computational resources to achieve a good estimate of the value of environment states....
Structured representation of complex stochastic systems (1998)
This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes....
Learning Belief Networks in the Presence of Missing Values and Hidden Variables (1997)
In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scenarios: learning both...
Modeling belief in dynamic systems. part i: Foundations (1997)
Nir Friedman, Joseph Y. Halpern
Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations...
Sequential Update of Bayesian Network Structure (1997)
Nir Friedman, Moises Goldszmidt
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains,...
Bayesian Network Classifiers (1997)
Nir Friedman, Dan Geiger, Moises Goldszmidt, G. Provan, P. Langley, P. Smyth
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with...
Challenge: Where is the Impact of Bayesian Networks in Learning? (1997)
Nir Friedman, Moises Goldszmidt, David Heckerman
Bayesian networks are graphical representations of probability distributions. Over the last decade, these representations have become the method of choice for representation of uncertainly in...
Modeling belief in dynamic systems. part i: Foundations (1997)
Nir Friedman, Joseph Y. Halpern
Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations...
Image segmentation in video sequences: A probabilistic approach (1997)
"Background subtraction" is an old technique for finding moving objects in a video sequence---for example, cars driving on a freeway. The idea is that subtracting the current image from a...
Challenge: Where is the Impact of Bayesian Networks in Learning? (1997)
Nir Friedman, Moises Goldszmidt, David Heckerman
Bayesian networks are graphical representations of probability distributions. Over the last decade, these representations have become the method of choice for representation of uncertainly in...
Bayesian Network Classifiers. (1997)
Nir Friedman, Dan Geiger, Moises Goldszmidt, Gregory Provan
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with...
Modeling Belief in Dynamic Systems. Part I: Foundations (1997)
Nir Friedman, Joseph Y. Halpern
Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations...
Sequential update of Bayesian network structure (1997)
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains,...
Bayesian network classifiers (1997)
Nir Friedman, Dan Geiger, Moises Goldszmidt
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with...
Context-specific independence in bayesian networks (1996)
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
First-order conditional logic revisited (1996)
Nir Friedman, Joseph Y. Halpern, Daphne Koller
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order...
Discretizing continuous attributes while learning bayesian networks (1996)
Nir Friedman, Moises Goldszmidt
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new...
Belief revision: a critique (1996)
Nir Friedman, Joseph Y. Halpern
The problem of belief change---how an agent should revise her beliefs upon learning new information---has been an active area of research in both philosophy and artificial intelligence. Many...
First-order conditional logic revisited (1996)
Nir Friedman, Joseph Y. Halpern, Daphne Koller
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order...
Building Classifiers using Bayesian Networks (1996)
Nir Friedman, Moises Goldszmidt
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of...
Learning Bayesian Networks with Local Structure (1996)
Nir Friedman, Moises Goldzsmidt
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and...
A Qualitative Markov Assumption and Its Implications for Belief Change (1996)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophyand AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly...
Plausibility Measures and Default Reasoning (1996)
Nir Friedman, Joseph Y. Halpern
this paper: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, ffl-semantics, possibilistic structures, and...
On the Sample Complexity of Learning Bayesian Networks (1996)
In recent years there has been an increasing interest in learning Bayesian networks from data. One of the most effective methods for learning such networks is based on the minimum description length...
Plausibility Measures and Default Reasoning (1996)
Nir Friedman, Joseph Y. Halpern
In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, ffl- semantics, possibilistic structures, and -rankings, that have been shown to be...
Belief Revision: A Critique (1996)
Nir Friedman, Joseph Y. Halpern
We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change...
Context-Specific Independence in Bayesian Networks (1996)
Craig Boutilier, Nir Friedman, Moises Goldszmidt, Daphne Koller
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
A Qualitative Markov Assumption and Its Implications for Belief Change (1996)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophyand AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly...
Belief Revision: A Critique (1996)
Nir Friedman, Joseph Y. Halpern
We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change...
Plausibility Measures and Default Reasoning (1996)
Nir Friedman, Joseph Y. Halpern
this paper: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, ffl-semantics, possibilistic structures, and...
Context-Specific Independence in Bayesian Networks (1996)
Craig Boutilier, Nir Friedman, Moises Goldszmidt, Daphne Koller
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
Context-Specific Independence in Bayesian Networks (1996)
Craig Boutilier, Nir Friedman, Moises Goldszmidt, Daphne Koller
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
Building Classifiers Using Bayesian Networks (1996)
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of...
Context-specific independence in bayesian networks (1996)
Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
Belief revision: a critique (1996)
Nir Friedman, Joseph Y. Halpern
The problem of belief change—how an agent should revise her beliefs upon learning new information—has been an active area of research in both philosophy and artificial intelligence. Many...
Learning Bayesian networks with local structure (1996)
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and...
Discretizing continuous attributes while learning bayesian networks (1996)
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new...
A qualitative Markov assumption and its implications for belief change (1996)
The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly...
A qualitative Markov assumption and its implications for belief change (1996)
The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly...
Plausibility measures and default reasoning (1996)
In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, -semantics, possibilistic structures, and ¡-rankings, that have been shown to be...
Context-specific independence in bayesian networks (1996)
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
On decision-theoretic foundations for defaults (1995)
Ronen I. Brafman, Nir Friedman
In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a...
Plausibility Measures: A User’s Guide (1995)
Nir Friedman, Joseph Y. Halpern
We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered...
Nondeterministic Actions and the Frame Problem (1995)
Craig Boutilier, Craig Boutilier, Nir Friedman, Nir Friedman
We describe a logical system and methodology for the natural specification of nondeterministic actions. The logic combines elements of dynamic logic, process logic and the situation calculus and...
On Decision-Theoretic Foundations for Defaults (1995)
Ronen I. Brafman, Nir Friedman
In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a...
Plausibility Measures: A User's Guide (1995)
Nir Friedman, Joseph Y. Halpern
We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered...
Nondeterministic Actions and the Frame Problem (1995)
Craig Boutilier, Craig Boutilier, Nir Friedman, Nir Friedman
We describe a logical system and methodology for the natural specification of nondeterministic actions. The logic combines elements of dynamic logic, process logic and the situation calculus and...
On Decision-Theoretic Foundations for Defaults (1995)
In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a...
On Decision-Theoretic Foundations for Defaults (1995)
In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a...
Plausibility Measures: A User’s Guide (1995)
We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered...
Nondeterministic actions and the frame problem (1995)
Craig Boutilier, Craig Boutilier, Nir Friedman, Nir Friedman
We describe a logical system and methodology for the natural specification of nondeterministic actions. The logic combines elements of dynamic logic, process logic and the situation calculus and...
Plausibility Measures: A User’s Guide (1995)
We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered...
On decision-theoretic foundations for defaults (1995)
In recent years considerable effort has gone into understanding default reasoning Most of this effort concentrated on the question of en tailment, 1 e, what conclusions are warranted by a...
Qualitative Planning under Assumptions: A Preliminary Report (1994)
Most planners constructed up to now are qualitative: they deal with uncertainty by considering all possible outcomes of each plan, without quantifying their relative likelihood. They then choose a...
A Knowledge-Based Framework for Belief Change - Part I: Foundations (1994)
Nir Friedman, Joseph Y. Halpern
We propose a general framework in which to study belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes ' if he knows that ' is true in all the...
On the Complexity of Conditional Logics (1994)
Nir Friedman, Joseph Y. Halpern
Conditional logics, introduced by Lewis and Stalnaker, have been utilized in artificial intelligence to capture a broad range of phenomena. In this paper we examine the complexity of several variants...
Conditional Logics of Belief Change (1994)
Nir Friedman, Joseph Y. Halpern
The study of belief changehas been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. Belief...
A Knowledge-Based Framework for Belief Change, Part II: Revision and Update (1994)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
On the Complexity of Conditional Logics (1994)
Nir Friedman, Joseph Y. Halpern
Conditional logics, introduced by Lewis and Stalnaker, have been utilized in artificial intelligence to capture a broad range of phenomena. In this paper we examine the complexity of several variants...
Conditional Logics of Belief Change (1994)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. Belief...
Conditional logics of belief change (1994)
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. Belief...
Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression
Marion, Rosa M., Regev, Aviv, Segal, Eran, Barash, Yoseph, Koller, Daphne, Friedman, Nir, ...
Yeast cells modulate their protein synthesis capacity in response to physiological needs through the transcriptional control of ribosomal protein (RP) genes. Here we demonstrate that the...
Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge
Kaplan, Tommy, Friedman, Nir, Margalit, Hanah
Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable...
Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae
Liu, Chih Long, Kaplan, Tommy, Kim, Minkyu, Buratowski, Stephen, Schreiber, Stuart L, Friedman, Nir, ...
Covalent modification of histone proteins plays a role in virtually every process on eukaryotic DNA, from transcription to DNA repair. Many different residues can be covalently modified, and it has...
Precise Temporal Modulation in the Response of the SOS DNA Repair Network in Individual Bacteria
Friedman, Nir, Vardi, Shuki, Ronen, Michal, Alon, Uri, Stavans, Joel
The SOS genetic network is responsible for the repair/bypass of DNA damage in bacterial cells. While the initial stages of the response have been well characterized, less is known about the dynamics...
Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression
Marion, Rosa M., Regev, Aviv, Segal, Eran, Barash, Yoseph, Koller, Daphne, Friedman, Nir, ...
Yeast cells modulate their protein synthesis capacity in response to physiological needs through the transcriptional control of ribosomal protein (RP) genes. Here we demonstrate that the...
Quantitative kinetic analysis of the bacteriophage λ genetic network
Kobiler, Oren, Rokney, Assaf, Friedman, Nir, Court, Donald L., Stavans, Joel, Oppenheim, Amos B.
The lysis–lysogeny decision of bacteriophage λ has been a paradigm for a developmental genetic network, which is composed of interlocked positive and negative feedback loops. This genetic network...
Precise Temporal Modulation in the Response of the SOS DNA Repair Network in Individual Bacteria
Friedman, Nir, Vardi, Shuki, Ronen, Michal, Alon, Uri, Stavans, Joel
The SOS genetic network is responsible for the repair/bypass of DNA damage in bacterial cells. While the initial stages of the response have been well characterized, less is known about the dynamics...
Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge
Kaplan, Tommy, Friedman, Nir, Margalit, Hanah
Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable...
Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae
Liu, Chih Long, Kaplan, Tommy, Kim, Minkyu, Buratowski, Stephen, Schreiber, Stuart L, Friedman, Nir, ...
Covalent modification of histone proteins plays a role in virtually every process on eukaryotic DNA, from transcription to DNA repair. Many different residues can be covalently modified, and it has...
A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval
Habib, Naomi, Kaplan, Tommy, Margalit, Hanah, Friedman, Nir
Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences...
Cell Cycle– and Chaperone-Mediated Regulation of H3K56ac Incorporation in Yeast
Kaplan, Tommy, Liu, Chih Long, Erkmann, Judith A., Holik, John, Grunstein, Michael, Kaufman, Paul D., ...
Acetylation of histone H3 lysine 56 is a covalent modification best known as a mark of newly replicated chromatin, but it has also been linked to replication-independent histone replacement. Here, we...
A Functional and Regulatory Map of Asthma
Novershtern, Noa, Itzhaki, Zohar, Manor, Ohad, Friedman, Nir, Kaminski, Naftali
The prevalence and morbidity of asthma, a chronic inflammatory airway disease, is increasing. Animal models provide a meaningful but limited view of the mechanisms of asthma in humans. A...
First-Order Conditional Logic Revisited
Nir Friedman, Joseph Y. Halpern, Daphne Koller
Conditional logics play an important role in recent attempts to investigate default reasoning. This paper investigates firstorder conditional logic. We show that, as for first-order probabilistic...
First-Order Conditional Logic Revisited
Nir Friedman, Joseph Y. Halpern, Daphne Koller
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order...
First-Order Conditional Logic Revisited
Nir Friedman, Joseph Y. Halpern, Daphne Koller
Conditional logics play an important role in recent attempts to investigate default reasoning. This paper investigates firstorder conditional logic. We show that, as for first-order probabilistic...
Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing
Yassour, Moran, Kaplan, Tommy, Fraser, Hunter B., Levin, Joshua Z., Pfiffner, Jenna, Adiconis, Xian, ...
Defining the transcriptome, the repertoire of transcribed regions encoded in the genome, is a challenging experimental task. Current approaches, relying on sequencing of ESTs or cDNA libraries, are...
Identifying novel constrained elements by exploiting biased substitution patterns
Garber, Manuel, Guttman, Mitchell, Clamp, Michele, Zody, Michael C., Friedman, Nir, Xie, Xiaohui
Motivation: Comparing the genomes from closely related species provides a powerful tool to identify functional elements in a reference genome. Many methods have been developed to identify conserved...
Nucleosome positioning from tiling microarray data
Yassour, Moran, Kaplan, Tommy, Jaimovich, Ariel, Friedman, Nir
Motivation: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory...