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Acceptance-rejection methods This post is based on chapter 1.4 of Advanced Markov Chain Monte Carlo. Another method of generating random variates from distributions is to use acceptance-rejection methods. Basically to generate a random number from , we generate a RN from an envelope distribution , where .  The acceptance-rejection algorithm is as follows: Repeat ... [Read more...]

An interesting note was arXived a few days ago by Madeleine Thompson and Radford Neal. Beside the nice touch of mixing crumbs and slices, the neat idea is to have multiple-try proposals for simulating within a slice and to decrease the dimension of the simulation space at each try. This ... [Read more...]

The book Random effects and latent variable model selection, edited by David Dunson in 2008 as a Springer Lecture Note. contains several chapters dealing with evidence approximation in mixed effect models. (Incidentally, I would be interested in the story behind the  Lecture Note as I found no explanation in the backcover ... [Read more...]

I’ve just received my copy of Advanced Markov Chain Monte Carlo Methods, by Liang, Liu, & Carroll. Although my PhD didn’t really involve any Bayesian methodology (and my undergrad was devoid of any Bayesian influence), I’ve found that the sort of problems I’m now tackling in systems ... [Read more...]

Following the posting on arXiv of the Statistical Science paper of Carvalho et al., and the publication by the same authors in Bayesian Analysis of Particle Learning for general mixtures I noticed on Hedibert Lopes’ website his rejoinder to the discussion of his Valencia 9 paper has been posted. Since the ... [Read more...]

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Here is a message sent by the organisers of MCMSki III in Utah next early January. When registering, make sure to tick the free registration for Adap’skiii as well! The fourth joint international meeting of the IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), nicknamed “... [Read more...]

Earlier this month, Daniel Sabanés Bové and Leo Held posted a paper about g-priors on arXiv. While I glanced at it for a few minutes, I did not have the chance to get a proper look at it till last Sunday. The g-prior was first introduced by the late ... [Read more...]

I have just learned that Julian Besag passed away last morning in Bristol after being admitted to the hospital two weeks ago. He was a leading figure of our field, a fiercely independent thinker, a brilliant statistician, and undoubtedly the clearest pretendent to having fathered MCMC. His influence on the ... [Read more...]

With too little time, too many things to do on my plate, here are some recent arXiv postings I won’t have time to comment: Adaptive Monte Carlo on multivariate binary sampling spaces. (arXiv:1008.0055v1) Mixture Decomposition of Distributions using a Decomposition of the Sample Space. (arXiv:1008.0204v1) Bayesian Model ... [Read more...]

Although the revision is quite minor, it took us two months to complete from the time I received the news in the Atlanta airport lounge… The vanilla Rao-Blackwellisation paper with Randal Douc has thus been resubmitted to the Annals of Statistics. And rearXived. The only significant change is the inclusion ... [Read more...]

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A few months ago, Latuszyński, Kosmidis, Papaspiliopoulos and Roberts arXived a paper I should have noticed earlier as its topic is very much related to our paper with Randal Douc on the vanilla Rao-Blackwellisation scheme. It is motivated by the Bernoulli factory problem, which aims at (unbiasedly) estimating f(... [Read more...]

The vanilla Rao-Blackwellisation paper with Randal Douc that had been resubmitted to the Annals of Statistics is now back for a revision, with quite encouraging comments: The paper has been reviewed by two referees both of whom comment on the clear exposition and the novelty of the results. Both referees ... [Read more...]

Following my remarks on the t-walk algorithm in the recent A General Purpose Sampling Algorithm for Continuous Distributions, published by Christen and Fox in Bayesian Analysis that acts like a general purpose MCMC algorithm, Darren Wraith tested it on the generic (10 dimension) banana target we used in the cosmology paper. ... [Read more...]

When I read in the abstract of the recent A General Purpose Sampling Algorithm for Continuous Distributions, published by Christen and Fox in Bayesian Analysis that We develop a new general purpose MCMC sampler for arbitrary continuous distributions that requires no tuning. I am slightly bemused. The proposal of the ... [Read more...]

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