Archive for Bayesian statistics

a refutation of Johnson’s PNAS paper

Posted in Books, Statistics, University life with tags , , , , , , , on February 11, 2014 by xi'an

Jean-Christophe Mourrat recently arXived a paper “P-value tests and publication bias as causes for high rate of non-reproducible scientific results?”, intended as a rebuttal of Val Johnson’s PNAS paper. The arguments therein are not particularly compelling. (Just as ours’ may sound so to the author.)

“We do not discuss the validity of this [Bayesian] hypothesis here, but we explain in the supplementary material that if taken seriously, it leads to incoherent results, and should thus be avoided for practical purposes.”

The refutation is primarily argued as a rejection of the whole Bayesian perspective. (Although we argue Johnson’ perspective is not that Bayesian…) But the argument within the paper is much simpler: if the probability of rejection under the null is at most 5%, then the overall proportion of false positives is also at most 5% and not 20% as argued in Johnson…! Just as simple as this. Unfortunately, the author mixes conditional and unconditional, frequentist and Bayesian probability models. As well as conditioning upon the data and conditioning upon the rejection region… Read at your own risk. Continue reading

Statistical modeling and computation [book review]

Posted in Books, R, Statistics, University life with tags , , , , , , , , , , , , , on January 22, 2014 by xi'an

Dirk Kroese (from UQ, Brisbane) and Joshua Chen (from ANU, Canberra) just published a book entitled Statistical Modeling and Computation, distributed by Springer-Verlag (I cannot tell which series it is part of from the cover or frontpages…) The book is intended mostly for an undergrad audience (or for graduate students with no probability or statistics background). Given that prerequisite, Statistical Modeling and Computation is fairly standard in that it recalls probability basics, the principles of statistical inference, and classical parametric models. In a third part, the authors cover “advanced models” like generalised linear models, time series and state-space models. The specificity of the book lies in the inclusion of simulation methods, in particular MCMC methods, and illustrations by Matlab code boxes. (Codes that are available on the companion website, along with R translations.) It thus has a lot in common with our Bayesian Essentials with R, meaning that I am not the most appropriate or least unbiased reviewer for this book. Continue reading

Dennis Lindley (1923-2013)

Posted in Books, Statistics, University life with tags , , , , , , on December 16, 2013 by xi'an

Dennis Lindley most sadly passed away yesterday at the hospital near his home in Somerset. He was one of the founding fathers of our field (of Bayesian statistics), who contributed to formalise Bayesian statistics in a coherent theory. And to make it one with rational decision-making, a perspective missing in Jeffreys’ vision. (His papers figured prominently in the tutorials we gave yesterday for the opening of O’Bayes 250.) At the age of 90, his interest in the topic had not waned away: as his interview with Tony O’Hagan last Spring showed, his passionate arguing for the rationale of the Bayesian approach was still there and alive! The review he wrote of The Black Swan a few years ago also demonstrated he had preserved his ability to see through bogus arguments. (See his scathing “One hardly advances the respect with which statisticians are held in society by making such declarations” in his ripping discussion of Aitkin’s 1991 Posterior Bayes factors.) He also started this interesting discussion last year about the five standard deviations “needed” for the Higgs boson…  My personal email contacts with Dennis over the re-reading of Jeffreys’ book  were a fantastic experience as he kindly contributed by expanding on how the book was received at the time and correcting some of my misunderstanding. It is a pity I can no longer send him the (soon to come?) final version of my Jeffreys-Lindley paradox paper as I intended to do. The email thomasbayes@gmail.com will no longer answer our queries… I figure there will be many testimonies and shared memories of his contributions and life at the Bayes-250 conference tomorrow. Farewell, Dennis, and I hope you now explore the paths of a more coherent world than ours!

arXiv bonanza!

Posted in Statistics, University life with tags , , on November 6, 2013 by xi'an

Today, there is an avalanche of interesting titles published on arXiv, enough fodder for weeks of ‘Oggin’…!

[1]  arXiv:1311.1189 [pdf, other] Statistical Inference in Hidden Markov Models using $k$-segment Constraints Michalis K. Titsias, Christopher Yau, Christopher C. Holmes Comments: 37 pages
[2]  arXiv:1311.1138 [pdf, other] Analysis of the Gibbs sampler for hierarchical inverse problems Sergios Agapiou, Johnathan M. Bardsley, Omiros Papaspiliopoulos, Andrew M. Stuart
[3]  arXiv:1311.1131 [pdf, ps, other] Compatible Weighted Proper Scoring Rules Peter G. M. Forbes Biometrika (2012) 99 (4): 989-994
[4]  arXiv:1311.1129 [pdf, other] Discussion of “Geodesic Monte Carlo on Embedded Manifolds” Simon Byrne, Mark Girolami, Persi Diaconis, Christof Seiler, Susan Holmes, Ian L. Dryden, John T. Kent, Marcelo Pereyra, Babak Shahbaba, Shiwei Lan, Jeffrey Streets, Daniel Simpson Comments: Discussion of arXiv:1301.6064. To appear in the Scandinavian Journal of Statistics. 18 pages
[5]  arXiv:1311.1067 [pdf, ps, other] Approximation of improper prior by vague priors Christele Bioche, Pierre Druilhet Comments: 30 pages
[7]  arXiv:1311.1039 [pdf, other] Maximum penalized likelihood estimation in semiparametric capture-recapture modelsThéo Michelot, Roland Langrock, Thomas Kneib, Ruth King
[8]  arXiv:1311.1033 [pdf, other]Nonparametric Bayesian models of hierarchical structure in complex networks Mikkel N. Schmidt, Tue Herlau, Morten Mørup
[9]  arXiv:1311.0907 [pdf, other] Bayesian inference on the Stiefel manifold Vinayak Rao, Lizhen Lin, David B. Dunson
[11]  arXiv:1311.1027 (cross-list from math.PR) [pdf, ps, other] Perfect Simulation of Determinantal Point Processes Laurent Decreusefond (LTCI), Ian Flint (LTCI), Kah Choon Low (LTCI)
[20]  arXiv:1311.0689 [pdf, other] Particle filter-based Gaussian Process Optimisation for Parameter Inference Johan Dahlin, Fredrik Lindsten
[21]  arXiv:1311.0686 [pdf, other] Second-order Particle MCMC for Bayesian Parameter Inference Johan Dahlin, Fredrik Lindsten, Thomas B. Schön
[22]  arXiv:1311.0674 [pdf, ps, other] On the use of marginal posteriors in marginal likelihood estimation via importance-sampling K. Perrakis, I. Ntzoufras, E.G. Tsionas

Bayesian introductions at IXXI

Posted in Mountains, Statistics, Travel, University life with tags , , , , , , on October 28, 2013 by xi'an

Ten days ago I did a lighting-fast visit to Grenoble for a quick introduction to Bayesian notions during a Bayesian day organised by Michael Blum. It was supported by IXXI, Rhône Alpes Complex Systems Institute, a light structure that favors interdisciplinary research to model complex sytems such as biological or social systems, technological networks… This was an opportunity to recycle my Budapest overview from Bayes 250th to Bayes 2.5.0. (As I have changed my email signature initial from X to IX, I further enjoyed the name IXXI!) More seriously, I appreciated (despite the too short time spent there!) the mix of perspectives and disciplines represented in this introduction, from Bayesian networks and causality in computer science and medical expert systems, to neurosciences and the Bayesian theory of mind, to Bayesian population genetics. And hence the mix of audiences. The part about neurosciences and social representations on others’ mind reminded me of the discussion with Pierre Bessières we had a year ago on France Culture. Again, I am quite sorry and apologetic for having missed part of the day and opportunities for discussions, simply because of a tight schedule this week…

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