**I**n connection with the special issue of Entropy I mentioned a while ago, Pierre Alquier (formerly of CREST) has written an introduction to the topic of approximate Bayesian inference that is worth advertising (and freely-available as well). Its reference list is particularly relevant. (The deadline for submissions is 21 June,)

## Archive for MCMC

## approximate Bayesian inference [survey]

Posted in Statistics with tags ABC, Approximate Bayesian computation, Bayesian statistics, CREST, entropy, expectation-propagation, Gibbs posterior, Langevin Monte Carlo, Laplace approximations, machine learning, Markov chain Monte Carlo, MCMC, PAC-Bayes, RIKEN, sequential Monte Carlo, special issue, survey, Tokyo, variational approximations on May 3, 2021 by xi'an## probability that a vaccinated person is shielded from COVID-19?

Posted in Books, Statistics, Travel, University life with tags arXiv, AstraZeneca, binomial distribution, conjugate priors, COVID-19, JAGS, MCMC, medrXiv, Moderna, Pfizer, Pierre Simon Laplace, Thomas Bayes, vaccine on March 10, 2021 by xi'an**O**ver my flight to Montpellier last week, I read an arXival on a Bayesian analysis of the vaccine efficiency. Whose full title is *“What is the probability that a vaccinated person is shielded from Covid-19? A Bayesian MCMC based reanalysis of published data with emphasis on what should be reported as `efficacy'”*, by Giulio D’Agostini and Alfredo Esposito. In short I was not particularly impressed.

“But the real point we wish to highlight, given the spread of distributions, is that we do not have enough data for drawing sound conclusion.”

The reason for this lack of enthusiasm on my side is that, while the authors’ criticism of an excessive precision in Pfizer, Moderna, or AstraZeneca press releases is appropriate, given the published confidence intervals are not claiming the same precision, a Bayesian reanalysis of the published outcome of their respective vaccine trial outcomes does not show much, simply because there is awfully little data, essentially two to four Binomial-like outcomes. Without further data, the modelling is one of a simple graph of Binomial observations, with two or three probability parameters, which results in a very standard Bayesian analysis that does depend on the modelling choices being made, from a highly unrealistic assumption of homogeneity throughout the population(s) tested for the vaccine(s), to a lack of hyperparameters that could have been shared between vaccinated populations. Parts of the arXival are unrelated and unnecessary, like the highly detailed MCMC algorithm for simulating the posterior (incl. JAGS code) to the reminiscence of Bayes’ and Laplace’s early rendering of inverse probability. (I find both interesting and revealing that arXiv, just like medRxiv, posts a warning on top of COVID related preprints.)

## congrats, Pierre!!!

Posted in Statistics with tags awards, Bayesian statistics, computational statistics, Guy Medal, honours, MCMC, Royal Statistical Society, RSS, SMC², unbiased MCMC on March 3, 2021 by xi'an## general perspective on the Metropolis–Hastings kernel

Posted in Books, Statistics with tags delayed rejection sampling, formalism, Hamiltonian Monte Carlo, HMC, MCMC, Metropolis-Hastings algorithm, non-reversible MCMC, NUTS, parallel tempering, PDMP, pseudo-marginal MCMC, reversible jump, UCL, University of Bristol on January 14, 2021 by xi'an[My Bristol friends and co-authors] Christophe Andrieu, and Anthony Lee, along with Sam Livingstone arXived a massive paper on 01 January on the Metropolis-Hastings kernel.

“Our aim is to develop a framework making establishing correctness of complex Markov chain Monte Carlo kernels a purely mechanical or algebraic exercise, while making communication of ideas simpler and unambiguous by allowing a stronger focus on essential features (…) This framework can also be used to validate kernels that do not satisfy detailed balance, i.e. which are not reversible, but a modified version thereof.”

A central notion in this highly general framework is, extending Tierney (1998), to see an MCMC kernel as a triplet involving a probability measure μ (on an extended space), an *involution* transform φ generalising the proposal step (i.e. þ²=id), and an associated acceptance probability ð. Then μ-reversibility occurs for

with the rhs involving the push-forward measure induced by μ and φ. And furthermore there is always a choice of an acceptance probability ð ensuring for this equality to happen. Interestingly, the new framework allows for mostly seamless handling of more complex versions of MCMC such as reversible jump and parallel tempering. But also non-reversible kernels, incl. for instance delayed rejection. And HMC, incl. NUTS. And pseudo-marginal, multiple-try, PDMPs, &c., &c. it is remarkable to see such a general theory emerging a this (late?) stage of the evolution of the field (and I will need more time and attention to understand its consequences).

## Rao-Blackwellisation in the MCMC era

Posted in Books, Statistics, University life with tags auxiliary variables, birthday, C.R. Rao, conditioning, David Blackwell, demarginalisation, International Statistical Review, MCMC, Monte Carlo Statistical Methods, Rao-Blackwell theorem, Rao-Blackwellisation on January 6, 2021 by xi'an**A** few months ago, as indicated on this blog, I was contacted by ISR editors to write a piece on Rao-Blackwellisation, towards a special issue celebrating Calyampudi Radhakrishna Rao’s 100th birthday. Gareth Roberts and I came up with this survey, now on arXiv, discussing different aspects of Monte Carlo and Markov Chain Monte Carlo that pertained to Rao-Blackwellisation, one way or another. As I discussed the topic with several friends over the Fall, it appeared that the difficulty was more in setting the boundaries. Than in finding connections. In a way anything conditioning or demarginalising or resorting to auxiliary variates is a form of Rao-Blackwellisation. When re-reading the JASA Gelfand and Smith 1990 paper where I first saw the link between the Rao-Blackwell theorem and simulation, I realised my memory of it had drifted from the original, since the authors proposed there an approximation of the marginal based on replicas rather than the original Markov chain. Being much closer to Tanner and Wong (1987) than I thought. It is only later that the true notion took shape. *[Since the current version is still a draft, any comment or suggestion would be most welcomed!]*