Advances in Probabilistic Reasoning, (9999)
This paper discusses multiple Bayesian networks representation paradigms for encoding, asymmetric independence assertions. We offer three contributions: (1) an inference mechanism that makes explicit...
On the toric algebra of graphical models (2009)
Dan Geiger, Christopher Meek, Bernd Sturmfels
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general...
Geiger, Dan, Meek, Christopher, Wexler, Ydo
We develop an hidden Markov model (HMM)-based algorithm for computing exact parametric and non-parametric linkage scores in larger pedigrees than was possible before. The algorithm is applicable...
DOI: 10.1159/0000XXXXX Maximum Likelihood Haplotyping for General Pedigrees (2008)
Nickolay Dovgolevsky, Dan Geiger
Haplotype data is valuable in mapping disease-susceptibility genes in the study of Mendelian and complex diseases. We present algorithms for inferring a most likely haplotype confi guration for...
Dan Geiger, Christopher Meek, Ydo Wexler
We develop a novel algorithm, called VIP*, for structured variational approximate inference. This algorithm extends known algorithms to allow efficient multiple potential updates for overlapping...
Panel construction for mapping in admixed populations via expected mutual information (2008)
Bercovici, Sivan, Geiger, Dan, Shlush, Liran, Skorecki, Karl, Templeton, Alan
Mapping by admixture linkage disequilibrium (MALD) is an economical and powerful approach for the identification of genomic regions harboring disease susceptibility genes in recently admixed...
We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential...
A Bayesian LOD Score for Linkage Analysis of Complex Diseases (2007)
Motivation: The emphasis of genetic linkage analysis is rapidly shifting from the task of locating genes responsible for mendelian diseases, for which one major gene causes the disease, to...
Ann Becker, Dan Geiger, Christopher Meek
We show that if a strictly positive joint probability distribution for a set of binary random variables factors according to a tree, then vertex separation represents all and only the independence...
A Distance-Based Branch and Bound Feature Selection Algorithm (2007)
Ari Frank Technion, Ari Frank, Dan Geiger, Zohar Yakhini
There is no known efficient method for selecting $k$ Gaussian features from $n$ which achieve the lowest Bayesian classification error. We show an example of how greedy algorithms faced with this...
On the toric algebra of graphical models (2006)
Geiger, Dan, Meek, Christopher, Sturmfels, Bernd
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general...
On the toric algebra of graphical models (2006)
Geiger, Dan, Meek, Christopher, Sturmfels, Bernd
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general...
Scheduling Mixed Workloads in Multi-grids: The Grid Execution Hierarchy (2006)
Mark Silberstein Dan, Dan Geiger, Assaf Schuster
Consider a workload in which massively parallel tasks that require large resource pools are interleaved with short tasks that require fast response but consume fewer resources. We aim at achieving...
Polyadenylation of ribosomal RNA in human cells (2006)
Slomovic, Shimyn, Laufer, David, Geiger, Dan, Schuster, Gadi
The addition of poly(A)-tails to RNA is a process common to almost all organisms. In eukaryotes, stable poly(A)-tails, important for mRNA stability and translation initiation, are added to the 3′...
Maximum likelihood haplotyping for general pedigrees. Human Heredity (2005)
Nickolay Dovgolevsky, Dan Geiger
Haplotype data is valuable in mapping disease-susceptibility genes, especially in the study of complex diseases. We present algorithms for inferring a most likely haplotype configuration for general...
Structured Variational Inference Procedures and their Realizations (2005)
Dan Geiger Computer, Dan Geiger
We describe and prove the convergence of several algorithms for approximate structured variational inference. We discuss the computation cost of these algorithms and describe their relationship to...
Maximum likelihood haplotyping for general pedigrees. Human Heredity (2005)
Nickolay Dovgolevsky, Dan Geiger
Haplotype data is valuable in mapping disease-susceptibility genes, especially in the study of complex diseases. We present algorithms for inferring a most likely haplotype configuration for general...
Efficient approximations for learning phylogenetic HMM models from data (2004)
Jojic, Vladimir, Jojic, Nebojsa, Meek, Chris, Geiger, Dan, Siepel, Adam, Haussler, David, ...
Motivation: We consider models useful for learning an evolutionary or phylogenetic tree from data consisting of DNA sequences corresponding to the leaves of the tree. In particular, we consider a...
Automated Analytic Asymptotic Evaluation of the Marginal (2003)
Likelihood For Latent, Dmitry Rusakov, Dan Geiger
We present two algorithms for analytic asymptotic evaluation of the marginal likelihood of data given a Bayesian network with hidden nodes. As shown by previous work, this evaluation is particularly...
We develop simple methods for constructing parameter priors for model choice among directed acyclic graphical (DAG) models. In particular, we introduce several assumptions that permit the...
Asymptotic Model Selection for Naive Bayesian Networks (2002)
We develop a closed form asymptotic formula to compute the marginal likelihood of data given a naive Bayesian network model with two hidden states and binary features.
Asymptotic model selection for naive Bayesian networks (2002)
Dmitry Rusakov, Dan Geiger, David Madigan
We develop a closed form asymptotic formula to compute the marginal likelihood of data given a naive Bayesian network model with two hidden states and binary features. This formula deviates from the...
Stratified exponential families: Graphical models and model selection (2001)
Geiger, Dan, Heckerman, David, King, Henry, Meek, Christopher
We describe a hierarchy of exponential families which is useful for distinguishing types of graphical models. Undirected graphical models with no hidden variables are linear exponential families...
On the parameter priors for discrete DAG models (2001)
We investigate parameter priors for discrete DAG models. It was shown in previous works that a Dirichlet prior on the parameters of a discrete DAG model is inevitable assuming global and local...
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...
Randomized Algorithms for the Loop Cutset Problem (2000)
Ann Becker, Reuven Bar-Yehuda, Dan Geiger
We showhow to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized...
Randomized Algorithms for the Loop Cutset Problem (2000)
Ann Becker Anyuta, Ann Becker, Dan Geiger
We show how to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized...
Density-Based Indexing for Approximate Nearest-Neighbor Queries (1999)
Kristin P. Bennett, Usama Fayyad, Dan Geiger
We consider the problem of performing nearest-neighbor queries efficiently over large high-dimensional databases. Assuming that a full database scan to determine the nearest neighbor entries is not...
We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models. In particular, we introduce several assumptions that permit the...
Automatic selection of loop breakers for genetic linkage analysis (1998)
Ann Becker, Dan Geiger, Alejandro A. Schaffer, Alejandro A. Schaffer
networks.
Automatic selection of loop breakers for genetic linkage analysis (1998)
Ann Becker, Dan Geiger, Alejandro A. Schaffer
Pedigree loops pose a difficult computational challenge in genetic linkage analysis. The most popular linkage analysis package, linkage, uses an algorithm that converts a looped pedigree into a...
Reuven Bar-Yehuda, Dan Geiger, Joseph (Seffi) Naor, Ron M. Roth
A feedback vertex set of an undirected graph is a subset of vertices that intersects with the vertex set of each cycle in the graph. Given an undirected graph G with n vertices and weights on its...
Stratified Exponential Families: Graphical Models and Model Selection (1998)
Dan Geiger, David Heckerman, Henry King, Christopher Meek, Redmond Wa
We provide a classification of graphical models according to their representation as exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs),...
Reuven Bar-Yehuda, DAN GEIGER, JOSEPH (SEFFI) NAOR, RON M. ROTH
.<F3.832e+05> A<F3.42e+05> feedback vertex set<F3.832e+05> of an undirected graph is a subset of vertices that intersects with the vertex set of each cycle in the graph. Given an...
We provide a new characterization of the Dirichlet distribution. Let $\theta_{ij}, 1 \leq i \leq k, 1 \leq j \leq n$, be positive random variables that sum to unity. Define $\theta_{i \cdot} =...
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...
Bayesian Network Classifiers. (1997)
Nir Friedman Nir, 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...
Finding Optimal Triangulations Via Minimal Vertex Separators (1997)
An algorithm called QuickTree is developed for finding a triangulation T of a given undirected graph G such that the size of T 's maximal clique is minimum and such that no other triangulation...
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...
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...
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning for inference. Our algorithm for finding a loop cutset,...
Dan Geiger, David Heckerman, Redmond Wa
this article is a characterization of the Dirichlet distribution based on local and global parameter independence and on the assumption that the probability distributions of all the parameters are...
Asymptotic Model Selection for Directed Networks with Hidden Variables (1996)
Dan Geiger, David Heckerman, Christopher Meek
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select...
A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees (1996)
An algorithm is developed for finding a close to optimal junction tree of a given graph G. The algorithm has a worst case complexity O(c k n a ) where a and c are constants, n is the number of...
Asymptotic Model Selection for Directed Networks with Hidden Variables (1996)
Dan Geiger, David Heckerman, Christopher Meek
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select...
Learning Bayesian networks: The combination of knowledge and statistical data (1995)
David Heckerman, Dan Geiger, David M. Chickering
We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative...
Learning Bayesian networks: The combination of knowledge and statistical data (1995)
David Heckerman, Dan Geiger, David M. Chickering
We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative...
A Characterization of the Dirichlet Distribution through Global and Local Independence (1995)
We provide a new characterization of the Dirichlet distribution. Let ` ij , 1 i k; 1 j n, be positive random variables that sum to unity. Define ` i\Delta = P n j=1 ` ij , `I \Delta = f` i\Delta g...
Likelihoods and Parameter Priors for Bayesian Networks (1995)
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce several assumptions that...
this technical claim is that in order to find all positive integrable functions that satisfy Eq. 9, it is permissible to take any derivative at any point in the domain because it exists. By setting z...
Learning Bayesian Networks (1995)
We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we develop simple methods for generating priors for...
Likelihoods and Parameter Priors for Bayesian Networks (1995)
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce several assumptions that...
A Characterization of the Bivariate Normal-Wishart Distribution (1995)
this article is that if local and global independence hold, and assuming a positive pdf f(~¯; W ), then f(~¯; W ) must be a normal-Wishart distribution. Therefore, local and global independence...
Approximation algorithms for the loop cutset problem (1994)
Ann Becker, Reuven Bar-yehuda, Dan Geiger
We show how to find a minimum loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in Pearl's method of conditioning for inference. Our random...
Approximation algorithms for the loop cutset problem (1994)
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning for inference. Our algorithm for finding a loop cutset,...
Learning Bayesian Networks is NP-Hard (1994)
David Chickering, Dan Geiger, David Heckerman
Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reflecting the goodness-of-fit of the structure...
Approximation Algorithms for the Loop Cutset Problem (1994)
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning for inference. Our algorithm for finding a loop cutset,...
Optimal satisficing tree searches (1991)
Dan Geiger, Jeffrey A. Barnett
We provide an algorithm that finds optimal search strategies for and trees and or trees. Our model includes three outcomes when a node is explored: (1) finding a solution, (2) not finding a solution...
Graphoids :--a qualitative framework for probabilistic inference /--by Dan Geiger. (1990)
Typescript (photocopy).
Towards the formalization of informational dependencies / (1987)
Thesis (M.S.)--University of California, Los Angeles, 1987.
Polyadenylation and Degradation of Human Mitochondrial RNA: the Prokaryotic Past Leaves Its Mark†
Slomovic, Shimyn, Laufer, David, Geiger, Dan, Schuster, Gadi
RNA polyadenylation serves a purpose in bacteria and organelles opposite from the role it plays in nuclear systems. The majority of nucleus-encoded transcripts are characterized by stable poly(A)...
Polyadenylation of ribosomal RNA in human cells
Slomovic, Shimyn, Laufer, David, Geiger, Dan, Schuster, Gadi
The addition of poly(A)-tails to RNA is a process common to almost all organisms. In eukaryotes, stable poly(A)-tails, important for mRNA stability and translation initiation, are added to the 3′...
Polyadenylation and Degradation of Human Mitochondrial RNA: the Prokaryotic Past Leaves Its Mark†
Slomovic, Shimyn, Laufer, David, Geiger, Dan, Schuster, Gadi
RNA polyadenylation serves a purpose in bacteria and organelles opposite from the role it plays in nuclear systems. The majority of nucleus-encoded transcripts are characterized by stable poly(A)...
Polyadenylation of ribosomal RNA in human cells
Slomovic, Shimyn, Laufer, David, Geiger, Dan, Schuster, Gadi
The addition of poly(A)-tails to RNA is a process common to almost all organisms. In eukaryotes, stable poly(A)-tails, important for mRNA stability and translation initiation, are added to the 3′...
A Deleterious Mutation in SAMD9 Causes Normophosphatemic Familial Tumoral Calcinosis
Topaz, Orit, Indelman, Margarita, Chefetz, Ilana, Geiger, Dan, Metzker, Aryeh, Altschuler, Yoram, ...
Familial tumoral calcinosis (FTC) is a rare autosomal recessive disorder characterized by the progressive deposition of calcified masses in cutaneous and subcutaneous tissues, which results in...
Lugassy, Jennie, Itin, Peter, Ishida-Yamamoto, Akemi, Holland, Kristen, Huson, Susan, Geiger, Dan, ...
Naegeli-Franceschetti-Jadassohn syndrome (NFJS) and dermatopathia pigmentosa reticularis (DPR) are two closely related autosomal dominant ectodermal dysplasia syndromes that clinically share complete...
Panel construction for mapping in admixed populations via expected mutual information
Bercovici, Sivan, Geiger, Dan, Shlush, Liran, Skorecki, Karl, Templeton, Alan
Mapping by admixture linkage disequilibrium (MALD) is an economical and powerful approach for the identification of genomic regions harboring disease susceptibility genes in recently admixed...
Nousbeck, Janna, Spiegel, Ronen, Ishida-Yamamoto, Akemi, Indelman, Margarita, Shani-Adir, Ayelet, Adir, Noam, ...
Single-gene disorders offer unique opportunities to shed light upon fundamental physiological processes in humans. We investigated an autosomal-recessive phenotype characterized by alopecia,...
Speeding up HMM algorithms for genetic linkage analysis via chain reductions of the state space
Geiger, Dan, Meek, Christopher, Wexler, Ydo
We develop an hidden Markov model (HMM)-based algorithm for computing exact parametric and non-parametric linkage scores in larger pedigrees than was possible before. The algorithm is applicable...