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Christian P. Robert

Publications and source records attributed to Christian P. Robert.

At least 91 records · Page 5Linked to original sources
arXiv2011

Adaptive Multiple Importance Sampling

The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earlier adaptive importance sampling implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each iteration, following the technique of the deterministic multiple mixture estimator of Owen and Zhou (2000). Although the convergence properties of the algorithm cannot be fully investigated, we demonstrate through a challenging banana shape target distribution and a population genetics example that the improvement brought by this technique is substantial.

stat.CO↗
arXiv2011

Lack of confidence in ABC model choice

Approximate Bayesian computation (ABC) have become a essential tool for the analysis of complex stochastic models. Earlier, Grelaud et al. (2009) advocated the use of ABC for Bayesian model choice in the specific case of Gibbs random fields, relying on a inter-model sufficiency property to show that the approximation was legitimate. Having implemented ABC-based model choice in a wide range of phylogenetic models in the DIY-ABC software (Cornuet et al., 2008), we now present theoretical background as to why a generic use of ABC for model choice is ungrounded, since it depends on an unknown amount of information loss induced by the use of insufficient summary statistics. The approximation error of the posterior probabilities of the models under comparison may thus be unrelated with the computational effort spent in running an ABC algorithm. We then conclude that additional empirical verifications of the performances of the ABC procedure as those available in DIYABC are necessary to conduct model choice.

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arXiv2011

Approximate Bayesian Computational methods

Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions made to the original ABC algorithm over the recent years.

stat.CO↗
arXiv2011

Simulation in Statistics

Simulation has become a standard tool in statistics because it may be the only tool available for analysing some classes of probabilistic models. We review in this paper simulation tools that have been specifically derived to address statistical challenges and, in particular, recent advances in the areas of adaptive Markov chain Monte Carlo (MCMC) algorithms, and approximate Bayesian calculation (ABC) algorithms.

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arXiv2011

Using parallel computation to improve Independent Metropolis--Hastings based estimation

In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent. In particular, we present improvements to the independent Metropolis--Hastings algorithm that significantly decrease the variance of any estimator derived from the MCMC output, for a null computing cost since those improvements are based on a fixed number of target density evaluations. Furthermore, the techniques developed in this paper do not jeopardize the Markovian convergence properties of the algorithm, since they are based on the Rao--Blackwell principles of Gelfand and Smith (1990), already exploited in Casella and Robert (1996), Atchade and Perron (2005) and Douc and Robert (2010). We illustrate those improvements both on a toy normal example and on a classical probit regression model, but stress the fact that they are applicable in any case where the independent Metropolis-Hastings is applicable.

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arXiv2011

A vanilla Rao--Blackwellization of Metropolis--Hastings algorithms

Casella and Robert [Biometrika 83 (1996) 81--94] presented a general Rao--Blackwellization principle for accept-reject and Metropolis--Hastings schemes that leads to significant decreases in the variance of the resulting estimators, but at a high cost in computation and storage. Adopting a completely different perspective, we introduce instead a universal scheme that guarantees variance reductions in all Metropolis--Hastings-based estimators while keeping the computation cost under control. We establish a central limit theorem for the improved estimators and illustrate their performances on toy examples and on a probit model estimation.

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arXiv2010

On Particle Learning

This document is the aggregation of six discussions of Lopes et al. (2010) that we submitted to the proceedings of the Ninth Valencia Meeting, held in Benidorm, Spain, on June 3-8, 2010, in conjunction with Hedibert Lopes' talk at this meeting, and of a further discussion of the rejoinder by Lopes et al. (2010). The main point in those discussions is the potential for degeneracy in the particle learning methodology, related with the exponential forgetting of the past simulations. We illustrate in particular the resulting difficulties in the case of mixtures.

stat.ME↗
arXiv2010

Exact Bayesian Analysis of Mixtures

In this paper, we show how a complete and exact Bayesian analysis of a parametric mixture model is possible in some cases when components of the mixture are taken from exponential families and when conjugate priors are used. This restricted set-up allows us to show the relevance of the Bayesian approach as well as to exhibit the limitations of a complete analysis, namely that it is impossible to conduct this analysis when the sample size is too large, when the data are not from an exponential family, or when priors that are more complex than conjugate priors are used.

stat.CO↗
arXiv2010

An attempt at reading Keynes' Treatise on Probability

The book A Treatise on Probability was published by John Maynard Keynes in 1921. It contains a critical assessment of the foundations of probability and of the current statistical methodology. As a modern reader, we review here the aspects that are most related with statistics, avoiding a neophyte's perspective on the philosophical issues. In particular, the book is quite critical of the Bayesian approach and we examine the arguments provided by Keynes, as well as the alternative he proposes. This review does not subsume the scholarly study of Aldrich (2008a) relating Keynes with the statistics community of the time.

math.ST↗
arXiv2010

About incoherent inference

In Templeton (2010), the Approximate Bayesian Computation (ABC) algorithm (see, e.g., Pritchard et al., 1999, Beaumont et al., 2002, Marjoram et al., 2003, Ratmann et al., 2009) is criticised on mathematical and logical grounds: "the [Bayesian] inference is mathematically incorrect and formally illogical". Since those criticisms turn out to be bearing on Bayesian foundations rather than on the computational methodology they are primarily directed at, we endeavour to point out in this note the statistical errors and inconsistencies in Templeton (2010), refering to Beaumont et al. (2010) for a reply that is broader in scope since it also covers the phylogenetic aspects of nested clade versus a model-based approach.

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arXiv2010

Evidence and Evolution: A Review

"Evidence and Evolution: the Logic behind the Science" was published in 2008 by Elliott Sober. It examines the philosophical foundations of the statistical arguments used to evaluate hypotheses in evolutionary biology, based on simple examples and likelihood ratios. The difficulty with reading the book from a statistician's perspective is the reluctance of the author to engage into model building and even less into parameter estimation. The first chapter nonetheless constitutes a splendid coverage of the most common statistical approaches to testing and model comparison, even though the advocation of the Akaike information criterion against Bayesian alternatives is rather forceful. The book also covers an examination of the "intelligent design" arguments against the Darwinian evolution theory, predictably if unnecessarily resorting to Popperian arguments to correctly argue that the creationist perspective fails to predict anything. The following chapters cover the more relevant issues of assessing selection versus drift and of testing for the presence of a common ancestor. While remaining a philosophy treatise, Evidence and Evolution is written in a way that is accessible to laymen, if rather unusual from a statistician viewpoint, and the insight about testing issues gained from Evidence and Evolution makes it a worthwhile read.

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arXiv2010

The Search for Certainty: a critical assessment

The Search for Certainty was published in 2009 by Krzysztof Burdzy. It examines the "philosophical duopoly" of von Mises and de Finetti at the foundation of probability and statistics and find this duopoly missing. This review exposes the weakness of the arguments presented in the book, it questions the relevance of introducing a new set of probability axioms from a methodological perspective, and it concludes at the lack of impact of this book on statistical foundations and practice.

math.ST↗
arXiv2010

Bayesian model comparison in cosmology with Population Monte Carlo

We use Bayesian model selection techniques to test extensions of the standard flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial perturbation models are considered. To that end, we calculate the Bayesian evidence in favour of each model using Population Monte Carlo (PMC), a new adaptive sampling technique which was recently applied in a cosmological context. The Bayesian evidence is immediately available from the PMC sample used for parameter estimation without further computational effort, and it comes with an associated error evaluation. Besides, it provides an unbiased estimator of the evidence after any fixed number of iterations and it is naturally parallelizable, in contrast with MCMC and nested sampling methods. By comparison with analytical predictions for simulated data, we show that our results obtained with PMC are reliable and robust. The variability in the evidence evaluation and the stability for various cases are estimated both from simulations and from data. For the cases we consider, the log-evidence is calculated with a precision of better than 0.08. Using a combined set of recent CMB, SNIa and BAO data, we find inconclusive evidence between flat LambdaCDM and simple dark-energy models. A curved Universe is moderately to strongly disfavoured with respect to a flat cosmology. Using physically well-motivated priors within the slow-roll approximation of inflation, we find a weak preference for a running spectral index. A Harrison-Zel'dovich spectrum is weakly disfavoured. With the current data, tensor modes are not detected; the large prior volume on the tensor-to-scalar ratio r results in moderate evidence in favour of r=0. [Abridged]

astro-ph.CO↗
arXiv2010

On the relevance of the Bayesian approach to Statistics

We argue here about the relevance and the ultimate unity of the Bayesian approach in a neutral and agnostic manner. Our main theme is that Bayesian data analysis is an effective tool for handling complex models, as proven by the increasing proportion of Bayesian studies in the applied sciences. We disregard in this essay the philosophical debates on the deeper meaning of probability and on the random nature of parameters as things of the past that do a disservice to the approach and are incomprehensible to most bystanders.

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arXiv2010

On computational tools for Bayesian data analysis

While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the current chapter details its practical aspects through a review of the computational methods available for approximating Bayesian procedures. Recent innovations like Monte Carlo Markov chain, sequential Monte Carlo methods and more recently Approximate Bayesian Computation techniques have considerably increased the potential for Bayesian applications and they have also opened new avenues for Bayesian inference, first and foremost Bayesian model choice.

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arXiv2010

Bayesian computational methods

In this chapter, we will first present the most standard computational challenges met in Bayesian Statistics, focussing primarily on mixture estimation and on model choice issues, and then relate these problems with computational solutions. Of course, this chapter is only a terse introduction to the problems and solutions related to Bayesian computations. For more complete references, see Robert and Casella (2004, 2009), or Marin and Robert (2007), among others. We also restrain from providing an introduction to Bayesian Statistics per se and for comprehensive coverage, address the reader to Robert (2007), (again) among others.

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arXiv2010

Bayesian Inference

This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal method for summarising uncertainty and making estimates and predictions using probability statements conditional on observed data and an assumed model (Gelman 2008). The Bayesian perspective is thus applicable to all aspects of statistical inference, while being open to the incorporation of information items resulting from earlier experiments and from expert opinions. We provide here the basic elements of Bayesian analysis when considered for standard models, refering to Marin and Robert (2007) and to Robert (2007) for book-length entries.1 In the following, we refrain from embarking upon philosophical discussions about the nature of knowledge (see, e.g., Robert 2007, Chapter 10), opting instead for a mathematically sound presentation of an eminently practical statistical methodology. We indeed believe that the most convincing arguments for adopting a Bayesian version of data analyses are in the versatility of this tool and in the large range of existing applications, rather than in those polemical arguments.

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