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block-wise pseudo-marginals

April 4, 2016

One justification for pseudo-marginal Metropolis-Hastings algorithms is the completion or demarginalisation of the initial target with the random variates used to compute the unbiased estimator of the target or likelihood. In a recent arXival, M.-N. Tran, Robert Kohn,  M. Quiroz and M. Villani explore the idea of only updating part of those auxiliary random variates, […]

the penalty method

July 7, 2016

“In this paper we will make conceptually simple generalization of Metropolis algorithm, by adjusting the acceptance ratio formula so that the transition probabilities are unaffected by the fluctuations in the estimate of [the acceptance ratio]…” Last Friday, in Paris-Dauphine, my PhD student Changye Wu showed me a paper of Ceperley and Dewing entitled the penalty […]

Bayesian model comparison with intractable constants

February 8, 2016

Richard Everitt, Adam Johansen (Warwick), Ellen Rowing and Melina Evdemon-Hogan have updated [on arXiv] a survey paper on the computation of Bayes factors in the presence of intractable normalising constants. Apparently destined for Statistics and Computing when considering the style. A great entry, in particular for those attending the CRiSM workshop Estimating Constants in a […]

pseudo slice sampling

November 26, 2015

The workshop in Warwick last week made me aware of (yet) another arXiv posting I had missed: Pseudo-marginal slice sampling by Iain Murray and Matthew Graham. The idea is to mix the pseudo-marginal approach of Andrieu and Roberts (2009) with a noisy slice sampling scheme à la Neal (2003). The auxiliary random variable u used […]

is there such a thing as optimal subsampling?

June 12, 2020

This idea of optimal thinnin and burnin has been around since the early days of the MCMC revolution and did not come up with a definite answer. For instance, from a pure estimation perspective, subsampling always increases the variance of the resulting estimator. My personal approach is to ignore both burnin and thinnin and rather […]