J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
Recent developments in Bayesian modelling of DNA sequence data for detecting natural selection at the amino acid level are presented. This article summarizes and discusses empirical model-based...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
A procedure for identifying data heterogeneity when fitting regression models is presented. The method is based on the SAR procedure, developed by Peña and Tiao (2002), and has three steps. First,...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
This paper deals with the detection of multiple changepoints for independent but non identically distributed observations, which are assumed to be modeled by a linear regression with normal errors....
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
Sequential Monte Carlo (SMC) methods are a class of importance sampling and resampling techniques designed to simulate from a sequence of probability distributions. These approaches have become very...
in the “Large p, Small n ” Paradigm (2008)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
I discuss Bayesian factor regression models with many explanatory variables. These models are of particular interest and applicability in problems of prediction, but also for elucidating underlying...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
The aim of many microarray experiments is to discover genes that exhibit similar behaviour, that is, co-express. A common approach to analysis is to apply generic clustering algorithms that produce a...
A Statistical Approach to Modeling Genomic Aberrations in Cancer Cells (2008)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
Whereas most cells in the body carry the normal complement of 23 chromosome pairs, the cells within a cancerous tumor very often present highly abnormal genomic structure. Deletions, amplifications,...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
For many applications of machine learning the goal is to predict the value of a vector c given the value of a vector x of input features. In a classification problem c represents a discrete class...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
The clustering problem has attracted much attention from both statisticians and computer scientists in the past fifty years. Methods such as hierarchical clustering and the K-means method are...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We present an efficient procedure for estimating the marginal likelihood of probabilistic models with latent variables or incomplete data. This method constructs and optimises a lower bound on the...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
For the standard regression setup, conventional tree models partition the predictor space into regions where the variable of interest Y, can be approximated by a constant. A treed model extends this...
Non-Stationary Spatial Modeling (2008)
J. M. Bernardo, J. O. Berger, A. P. Dawid, D. Higdon, J. Swall, ...
Standard geostatistical models assume stationarity and rely on a variogram model to account for the spatial dependence in the observed data. In some instances, this assumption that the spatial...
J. M. Bernardo, J. O. Berger, A. P. Dawid
We thank the discussants for their kind comments and questions, and the interesting questions raised. We address the contributions by named discussants. DANIEL PE~NA Professor Pe~na contributes some...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
The integrated likelihood (also called the marginal likelihood or the normalizing constant) is a central quantity in Bayesian model selection and model averaging. It is defined as the integral over...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
The integrated likelihood (also called the marginal likelihood or the normalizing constant) is a central quantity in Bayesian model selection and model averaging. It is defined as the integral over...
J. M. Bernardo, J. O. Berger, A. P. Dawid, John Geweke
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The objectives are to clarify the Bayesian interpretation of non-Bayesian diagnostic tests, and provide...
The eld of statistics has seen many well-meaning crusades against threats from metaphysics and other heresy. In its founding prospectus of 1834, the Statistical Society of London has resolved \... to...
Density Modeling and Clustering Using Dirichlet Diffusion Trees (2008)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
I introduce a family of prior distributions over multivariate distributions, based on the use of a “Dirichlet diffusion tree ” to generate exchangeable data sets. These priors can be viewed as...
Bayesian Inference on Latent Structure in Time Series (2008)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Omar Aguilar, Gabriel Huerta, ...
A range of developments in Bayesian time series modelling in recent years has focussed on issues of identifying latent structure in time series. This has led to new uses and interpretations of...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We propose a new method for making inference about an unknown measure Γ(dλ) upon observing some values of the Fredholm integral g(ω) = � k(ω, λ)Γ(dλ) of a known kernel k(ω, λ), using Lévy...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems
Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals (2008)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
Hybrid Monte Carlo (HMC) is often the method of choice for computing Bayesian integrals that are not analytically tractable. However the success of this method may require a very large number of...
Bayesian Inference on Latent Structure in Time Series (2008)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Omar Aguilar, Gabriel Huerta, ...
A range of developments in Bayesian time series modelling in recent years has focussed on issues of identifying latent structure in time series. This has led to new uses and interpretations of...
Nonparametric Function Estimation Using Overcomplete Dictionaries (2008)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We consider the problem of estimating an unknown function based on noisy data using nonparametric regression. One approach to this estimation problem is to represent the function in a series...
The Logic of Counterfactuals in Causal Inference (2007)
Discussion Of Causal, A. P. Dawid
antics and well-founded logic, and many practical problems that long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. In the paper before us,...
J. M. Bernardo, J. O. Berger, A. P. Dawid
s would be very helpful. A simple example will serve to illustrate the effect of the choice of transformation group. Suppose ß(x) = N 2 ` 0; ` 1 ae ae 1 " with x = (x 1 ; x 2 ). The lag-one...
Report on HSSS Short Visit: Robust Bayes Inference and Generalised Maximum Entropy (2007)
.24> L(x; a) where L is some given loss function. If Nature plays strategy P , then Statistician's expected loss is EP L(X; a). A careful Statistician may be interested in deciding on the act...
<e-267> Merlise A. Clyde (2007)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Merlise A. Clyde
INTRODUCTION<E-186> Linear regression and its generalizations are some of the most commonly used methods in<E-442> the sciences for finding relationships between explanatory variables and...
Biometrics Spiegelhalter, A. Thomas, J. O. Berger, A. P. Dawid
Bibliography
stroudwhart on. upenn. edu SUMMARY (2007)
J. M. Bernardo, J. O. Be!ler, A. P. Dawid, D. Heckerman, ...
This paper develops a methodology for parameter and state variable inference using both asset and derivative price information. We combine theoretical pricing models and asset dynamics to generate a...
Expected Utility Estimation (2007)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We discuss practical methods for the assessment, comparison and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future...
Expected Utility Estimation (2007)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We discuss practical methods for the assessment, comparison and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future...
CWI Amsterdam, The Netherlands. (2007)
Suppose that, for purposes of inductive inference or choosing an optimal decision, we wish to select a single distribution P to act as representative of a class of such distributions. The Maximum...
Bayesian Models for Massive Multimedia Databases: a New Frontier (2007)
J. M. Bernardo, J. O. Be!er, A. P. Dawid, D. Heckerman, ...
Modelling the increasing number of digital databases (the web, photo-libraries, music collections, news archives, medical databases) is one of the greatest challenges of statisticians in the new...
Markov Random Field Extensions using State Space Models (2007)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We elaborate on the link between state space models and (Gaussian) Markov random fields. We extend the Markov random field models by generalising the corresponding state space model. It turns out...
chrsst at. duke. edu SUMMARY (2007)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems
A Nonparametric Bayesian Approach to Inverse Problems (2007)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We propose a new method for making inference about an unknown measure #(d#) upon observing some values of the Fredholm integral g(#) = k(#, #)#(d#) of a known kernel k(#, #), using Levy random fields...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, Matthew J. Beal, Zoubin Ghahramani
We present an ecient procedure for estimating the marginal likelihood of probabilistic models with latent variables or incomplete data. This method constructs and optimises a lower bound on the...
Hybrid Monte Carlo for Expensive Bayesian Integrals (2007)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, Carl Edward Rasmussen
Hybrid Monte Carlo (HMC) is often the method of choice for computing Bayesian integrals that are not analytically tractable. However the success of this method may require a very large number of...
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We present an efficient procedure for estimating the marginal likelihood of probabilistic models with latent variables or incomplete data. This method constructs and optimises a lower bound on the...
Markov Random Field Extensions using State Space Models (2007)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
We elaborate on the link between state space models and (Gaussian) Markov random fields. We extend the Markov random field models by generalising the corresponding state space model. It turns out...
BAYESIAN STATISTICS 6, pp. 000--000 (2007)
J. M. Bernardo, J. O. Berger, A. P. Dawid
We congratulate the authors with this important work on capturing latent structures in time series. The paper and it's predecessors are prominent contributions in the exploration of the...
Here we identify a class of problems based on the log score function in a mostly Bayesian context. We show that the Bayesian mixture density, the marginal density for the data, is an approximate...
J. M. Bernardo, J. O. Berger, A. P. Dawid, John Geweke
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The objectives are to clarify the Bayesian interpretation of non-Bayesian diagnostic tests, and provide...
BAYESIAN STATISTICS 7, pp. 000--000 (2007)
Bernardo Bayarri Berger, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
this article is a natural model for point processes whose events combine irregular bursts of activity with predictable (e.g. daily and hourly) patterns
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
SUMMARY This paper presents a new finite-dimensional Bayesian filter. The filter calculates the exact analytical expression for the posterior probability density function (pdf) of static systems with...
Identifying the consequences of dynamic treatment strategies (2005)
We formulate the problem of learning and comparing the effects of dynamic treatment strategies in a probabilistic decision-theoretic framework, and in particular show how Robins’s “G-computation...
Probability, Causality and the Empirical World: A Bayes–de Finetti–Popper– Borel Synthesis (2004)
This article expounds a philosophical approach to Probability and Causality: a synthesis of the personalist Bayesian views of de Finetti and Popper’s falsificationist programme. A falsification...
Meester & Sjerps (2003 Law, Probability and Risk 2, 51–62) (henceforth M & S) draw attention to an ambiguity in the definition of ‘the likelihood ratio’ that can arise with forensic multiple...
An object-oriented Bayesian network for estimating mutation rates (2003)
We describe the use of the object-oriented Hugin 6 probabilistic expert system software to structure the problem of estimating mutation rates on the basis of family data when paternity can not be...
Game Theory, Maximum Entropy, Minimum Discrepancy, and Robust Bayesian Decision Theory (2003)
We describe and develop a close relationship between two problems that have customarily been regarded as distinct: that of maximizing entropy, and that of minimizing worst-case expected loss. Using a...
The Variational Bayesian EM Algorithm for Incomplete Data: With Application to Scoring . . . (2003)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
this paper we describe the use of variational methods to approximate the marginal likelihood and posterior distributions of complex models. Variational methods, which have been used extensively in...
Bayesian Harmonic Models for Musical Signal Analysis (2003)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
This paper is concerned with the Bayesian analysis of musical signals. The ultimate aim is to use Bayesian hierarchical structures in order to infer quantities at the highest level, including such...
Hierarchical Bayesian Models for Applications in Information retrieval (2003)
J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, ...
this article, we find that most of the factors are very close to # while four of the factors achieve significant expected counts. Looking at the distribution over words, z), for those four factors,...
Optimal dynamic treatment regimes - Discussion on the paper by Murphy (2003)
Arjas, E., Jennison, C., Dawid, A. P., Cox, D. R., Cowell, R. G., Didelez, V., ...
Discussion of the papers by Rissanen and by (2000)
It is 12 years since I opened the discussion at the Royal Statistical Society meeting at which Rissanen and Wallace and Freeman presented companion papers on topics not far removed from those under...
mantics and well-founded logic, and many practical problems that long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. In the paper before us,...
Bayesian and Frequentist Approaches to Parametric Predictive Inference (1999)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Richard L. Smith
this paper, however, shows that this is too simple a conclusion. For many models, when assessed
Bayesian and Frequentist Approaches to Parametric Predictive Inference (1999)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Richard L. Smith
this paper, however, shows that this is too simple a conclusion. For many models, when assessed from the point of view of mean squared error of predictive probabilities, the plug-in approach is...
Bayesian analysis of Cepheid variable data. Bayesian Statistics 6 (1999)
J. M. Bernardo, J. O. Berger, A. P. Dawid, William H. Jefferys, ...
Cepheids are a type of variable star that play a key role in establishing the astronomical distance scale. These objects undergo regular pulsations, shrinking and expanding as their luminosities and...
Discussion of the Papers by Rissanen and by Wallace and Dowe (1999)
It is 12 years since I opened the discussion at the Royal Statistical Society meeting at which Rissanen and Wallace and Freeman presented companion papers on topics not far removed from those under...
Exact sampling for Bayesian inference: towards general purpose algorithms (1998)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Peter J. Green, DUNCAN J. MURDOCH
this paper is to describe recent efforts to construct exact sampling methods for continuous-state Markov chains. We review existing methods based on gamma-coupling and rejection sampling (Murdoch and...
Non-Stationary Spatial Modeling (1998)
J. M. Bernardo, J. O. Berger, A. P. Dawid, D. Higdon, J. Swall, ...
this paper, we propose an alternative model for accounting for heterogeneity in the
Exact sampling for Bayesian inference: towards general purpose algorithms (1998)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Peter J. Green, Duncan J. Murdoch
this paper we discuss the implementation of coupling from the past for samplers on a continuous state space; our ultimate objective is Bayesian MCMC with guaranteed convergence. We make some progress...
Bayesian Analysis of Animal Abundance Data via MCMC (1998)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Stephen P. Brooks
this paper we illustrate how a band-return model may be used to model the mallard data of Freeman and Morgan (1992). We show how the Gibbs sampler may be used to draw 2 S. P. Brooks approximate...
Uncertainty Analysis and other Inference Tools for Complex Computer Codes (1998)
Anthony O'Hagan, J. M. Bernardo, J. O. Berger, A. P. Dawid, Marc C. Kennedy, ...
This paper builds on work by Haylock and O'Hagan which developed a Bayesian approach to uncertainty analysis. The generic problem is to make posterior inference about the output of a complex...
On the Automatic Choice of Reversible Jumps (1998)
J. M. Bernardo, J. O. Berger, A. P. Dawid, Paolo Giudici, Gareth Roberts
this paper we will consider a mechanism for guiding the proposal choice by analysis of acceptance probabilities for jumps. Essentially the method involves an approximation for the acceptance...
Extremes of Mixed Environmental Processes (1998)
J. M. Bernardo, J. O. Berger, A. P. Dawid, David Walshaw
this paper we consider the problems posed by extremes generated by more than one distinct process. There is considerable evidence that a number of environmental variables generally have record...
Conditional Independence (1997)
This article has been prepared as an entry for the Wiley Encyclopedia of Statistical Sciences (Update). It gives a brief overview of fundamental properties and applications of conditional...
Prequential Probability: Principles and Properties (1997)
A. P. Dawid, V. G. Vovk, Royal Holloway
this paper we first illustrate the above considerations for a variety of appealling criteria, and then, in an attempt to understand this behaviour, introduce a new game-theoretic framework for...
Conditional Independence For Statistics And AI (1997)
) if P (A " B) = P (A)P (B): (1) Since P (AjB) = P (A " B)=P (B), this is equivalent to P (AjB) = P (A); (2) i.e. uncertainty about A is not changed on learning that B holds ---a judgment...
Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments (1992)
John Geweke, J. O. Berger, A. P. Dawid
Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither...
Rejoinder Richard Smith, J. M. Bernardo, J. O. Berger, A. P. Dawid, Richard L. Smith
this paper in Valencia.
Some matrix-variate distribution theory: Notational considerations and a Bayesian application (1981)
We introduce and justify a convenient notation for certain matrix-variate distributions which, by its emphasis on the important underlying parameters, and the theory on which it is based, eases...
Invariant distributions and analysis of variance models (1977)
Subjective joint distributions over the outcomes of a set of possible observations, arising from a classical analysis of variance layout, are considered. These will often incorporate invariance...
Posterior expectations for large observations (1973)
For a single observation X = x from a distribution having unknown location parameter Θ we investigate the asymptotic behaviour of the posterior distribution of Θ, as x tends to infinity. Conditions...
Un-Bayesian implications of improper Bayes inference in routine statistical problems (1972)
For two routine statistical problems, inference about the ratio of two exponential means and inference about the coefficient of variation of a normal random variable, a serious pathology of Bayesian...
Expectation consistency of inverse probability distributions (1972)
Inverse probability distributions for inference about a discrete parameter with an infinity of possible values are considered, based on discrete data. Their expectation consistency with the...
Asymptotic Properties of Conjugate Bayes Discrete Discrimination
This paper studies a problem of discrimination between two populations with binary variables. The number of variables which can be observed is allowed to tend to infinity. Assuming the Dirichlet...
Conjugate Bayes discrimination with infinitely many variables
The problem considered is that of discrimination between two multivariate normal populations, with common dispersion structure, when the number of variables that can be observed is unlimited. We...
Invariance and independence in multivariate distribution theory
Several general results are presented whereby various properties of independence or conditional independence between certain random variables may be deduced from the symmetries enjoyed by their joint...
Extendibility of spherical matrix distributions
We investigate the structure of distributions for matrices which can be embedded in arbitrarily large matrices whose distributions have properties of invariance under orthogonal rotations.