Category: Monte Carlo Statistical Methods

likelihood free nested sampling

A recent paper by Mikelson and Khammash found on bioRxiv considers the (paradoxical?) mixture of nested sampling and intractable likelihood. They however cover only the case when a particle filter or another unbiased estimator of the likelihood function. Unless I am missing something in the paper, this seems a very costly and convoluted approach when […]

EntropyMCMC [R package]

My colleague from the Université d’Orléans, Didier Chauveau, has just published on CRAN a new R package called EntropyMCMC, which contains convergence assessment tools for MCMC algorithms, based on non-parametric estimates of the Kullback-Leibler divergence between current distribution and target. (A while ago, quite a while ago!, we actually collaborated with a few others on […]

BayesComp 20: call for contributed sessions!

Just to remind readers of the incoming deadline for BayesComp sessions: The deadline for providing a title and brief abstract that the session is April 1, 2019. Please provide the names and affiliations of the organizer and the three speakers (the organizer can be one of them). Each session lasts 90 minutes and each talk […]

a new rule for adaptive importance sampling

Art Owen and Yi Zhou have arXived a short paper on the combination of importance sampling estimators. Which connects somehow with the talk about multiple estimators I gave at ESM last year in Helsinki. And our earlier AMIS combination. The paper however makes two important assumptions to reach optimal weighting, which is inversely proportional to […]

simulation fodder for future exams

Here are two nice exercises for a future simulation exam, seen and solved on X validated.The first one is about simulating a Gibbs sampler associated with the joint target exp{-|x|-|y|-a(y-x|} defined over IR² for a≥0 (or possibly a>-1). The conditionals are identical and non-standard, but a simple bound on the conditional density is the corresponding […]

Computational Bayesian Statistics [book review]

This Cambridge University Press book by M. Antónia Amaral Turkman, Carlos Daniel Paulino, and Peter Müller is an enlarged translation of a set of lecture notes in Portuguese. (Warning: I have known Peter Müller from his PhD years in Purdue University and cannot pretend to perfect objectivity. For one thing, Peter once brought me frozen-solid […]

revisiting the Gelman-Rubin diagnostic

Just before Xmas, Dootika Vats (Warwick) and Christina Knudson arXived a paper on a re-evaluation of the ultra-popular 1992 Gelman and Rubin MCMC convergence diagnostic. Which compares within-variance and between-variance on parallel chains started from hopefully dispersed initial values. Or equivalently an under-estimating and an over-estimating estimate of the MCMC average. In this paper, the […]