# Posts Tagged ‘ Bayes factor ’

## structure and uncertainty, Bristol, Sept. 26

September 26, 2012
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Another day full of interesting and challenging—in the sense they generated new questions for me—talks at the SuSTain workshop. After another (dry and fast) run around the Downs; Leo Held started the talks with one of my favourite topics, namely the theory of g-priors in generalized linear models. He did bring a new perspective on

## PLoS topic page on ABC

June 6, 2012
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A few more comments on the specific entry on ABC written by Mikael Sunnåker et al…. The entry starts with the representation of the posterior probability of an hypothesis, rather than with the posterior density of a model parameter, which seems to lead the novice reader astray. After all, (a) ABC was not introduced for

## Large-scale Inference

February 23, 2012
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Large-scale Inference by Brad Efron is the first IMS Monograph in this new series, coordinated by David Cox and published by Cambridge University Press. Since I read this book immediately after Cox’ and Donnelly’s Principles of Applied Statistics, I was thinking of drawing a parallel between the two books. However, while none of them can

## Harmonic means, again again

January 9, 2012
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Another arXiv posting I had had no time to comment is Nial Friel’s and Jason Wyse’s “Estimating the model evidence: a review“. This is a review in the spirit of two of our papers, “Importance sampling methods for Bayesian discrimination between embedded models” with Jean-Michel Marin (published in Jim Berger Feitschrift, Frontiers of Statistical Decision

## Approximate Bayesian computational methods on-line

October 25, 2011
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Fig. 4 – Boxplots of the evolution of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time

## Bayes factors and martingales

August 10, 2011
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$Bayes factors and martingales$

A surprising paper came out in the last issue of Statistical Science, linking martingales and Bayes factors. In the historical part, the authors (Shafer, Shen, Vereshchagin and Vovk) recall that martingales were popularised by Martin-Löf, who is also influential in the theory of algorithmic randomness. A property of test martingales (i.e., martingales that are non

## About Fig. 4 of Fagundes et al. (2007)

July 12, 2011
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Yesterday, we had a meeting of our EMILE network on statistics for population genetics (in Montpellier) and we were discussing our respective recent advances in ABC model choice. One of our colleagues mentioned the constant request (from referees) to include the post-ABC processing devised by Fagundes et al. in their 2007 ABC paper. (This paper

## Lack of confidence [revised]

April 21, 2011
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Following the comments on our earlier submission to PNAS, we have written (and re-arXived) a revised version where we try to spell out (better) the distinction between ABC point (and confidence) estimation and ABC model choice, namely that the problem was at another level for Bayesian model choice (using posterior probabilities). When doing point estimation

## Model weights for model choice

February 9, 2011
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$Model weights for model choice$

An ‘Og reader. Emmanuel Charpentier, sent me the following email about model choice: I read with great interest your critique of Peter Congdon’s 2006 paper (CSDA, 50(2):346-357) proposing a method of estimation of posterior model probabilities based on improper distributions for parameters not present in the model inder examination, as well as a more general

## ABC model choice not to be trusted [3]

January 30, 2011
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On Friday, I received a nice but embarrassing email from Xavier Didelot. He indeed reminded me that I attended the talk he gave at the model choice workshop in Warwick last May, as, unfortunately but rather unsurprisingly giving my short span memory!, I had forgotten about it! Looking at the slides he joined to his