**A**s I was asked to write a chapter on MCMC methods for an incoming *Handbook of Computational Statistics and Data Science*, published by Wiley, rather than cautiously declining!, I decided to recycle the answers I wrote on X validated to what I considered to be the most characteristic misunderstandings about MCMC and other computing methods, using as background the introduction produced by Wu Changye in his PhD thesis. Waiting for the opinion of the editors of the *Handbook* on this Q&A style. The outcome is certainly lighter than other recent surveys like the one we wrote with Peter Green, Krys Latuszinski, and Marcelo Pereyra, for Statistics and Computing, or the one with Victor Elvira, Nick Tawn, and Changye Wu.

## Archive for Gibbs sampling

## MCMC, with common misunderstandings

Posted in Books, pictures, R, Statistics, University life with tags ABC, Bayesian computing, computational statistics, Gibbs sampling, Handbook of Computational Statistics and Data Science, HMC, IMS Lawrence D. Brown PhD Student Award, MCMC, PhD thesis, Q&A format, Statistics and Computing, survey, variational Bayes methods on January 27, 2020 by xi'an## Scott Sisson’s ABC seminar in Paris [All about that Bayes]

Posted in pictures, Statistics, Travel, University life with tags ABC, AgroParisTech, approximate Bayesian inference, Australia, France, Gibbs sampling, New South Wales, Paris, Scott Sisson, seminar, Sydney, UNSW on January 20, 2020 by xi'an**O**n the “All about that Bayes” seminar tomorrow (Tuesday 21 at 3p.m., room 42, AgroParisTech, 16 rue Claude Bernard, Paris 5ième), Scott Sisson, School of Mathematics and Statistics at UNSW, and visiting Paris-Dauphine this month, will give a talk on

Approximate posteriors and data for Bayesian inference

Abstract

For various reasons, including large datasets and complex models, approximate inference is becoming increasingly common. In this talk I will provide three vignettes of recent work. These cover a) approximate Bayesian computation for Gaussian process density estimation, b) likelihood-free Gibbs sampling, and c) MCMC for approximate (rounded) data.

## stochastic magnetic bits, simulated annealing and Gibbs sampling

Posted in Statistics with tags factorisation, Gibbs sampling, Nature, optimisation, p-bits, prime numbers, quantum comuting, quantum cryptograpy, Series B, simulated annealing on October 17, 2019 by xi'an**A** paper by Borders et al. in the 19 September issue of Nature offers an interesting mix of computing and electronics and optimisation. With two preparatory tribunes! One [rather overdone] on Feynman’s quest. As a possible alternative to quantum computers for creating probabilistic bits. And making machine learning (as an optimisation program) more efficient. And another one explaining more clearly what is in the paper. As well as the practical advantage of the approach over quantum computing. As for the paper itself, the part I understood about factorising an integer F via minimising the squared difference between a product of two integers and F and using simulated annealing sounded rather easy, while the part I did not about constructing a semi-conductor implementing this stochastic search sounded too technical (especially in the métro during rush hour). Even after checking the on-line supplementary material. Interestingly, the paper claims for higher efficiency thanks to asynchronicity than a regular Gibbs simulation of Boltzman machines, quoting Roberts and Sahu (1997) without further explanation and possibly out of context (as the latter is not concerned with optimisation).

## ABC in Clermont-Ferrand

Posted in Mountains, pictures, Statistics, Travel, University life with tags ABC, ABC-Gibbs, Approximate Bayesian computation, Auvergne, Clermont-Ferrand, conditional sufficiency, cosmostats, dimension reduction, Gibbs sampling, likelihood-free methods, PMC, volcano on September 20, 2019 by xi'an**T**oday I am taking part in a one-day workshop at the Université of Clermont Auvergne on ABC. With applications to cosmostatistics, along with Martin Kilbinger [with whom I worked on PMC schemes], Florent Leclerc and Grégoire Aufort. This should prove a most exciting day! (With not enough time to run up Puy de Dôme in the morning, though.)

## the three i’s of poverty

Posted in Books, pictures, Statistics, Travel, University life with tags Gibbs sampling, loss function, Marseille, mixture of distributions, thesis defence, three i's of poverty on September 15, 2019 by xi'an**T**oday I made a “quick” (10h door to door!) round trip visit to Marseille (by train) to take part in the PhD thesis defense (committee) of Edwin Fourrier-Nicolaï, which title was *Poverty, inequality and redistribution: an econometric approach*. While this was mainly a thesis in economics, meaning defending some theory on inequalities based on East German data, there were Bayesian components in the thesis that justified (to some extent!) my presence in the jury. Especially around mixture estimation by Gibbs sampling. (On which I started working almost exactly 30 years ago, when I joined Paris 6 and met Gilles Celeux and Jean Diebolt.) One intriguing [for me] question stemmed from this defense, namely the notion of a Bayesian estimation of a *three i’s of poverty* (TIP) curve. The three i’s stand for incidence, intensity, and inequality, as, introduced in Jenkins and Lambert (1997), this curve measure the average income loss from the poverty level for the *100p*% lower incomes, when p varies between 0 and 1. It thus depends on the distribution F of the incomes and when using a mixture distribution its computation requires a numerical cdf inversion to determine the income *p*-th quantile. A related question is thus on how to define a Bayesian estimate of the TIP curve. Using an average over the values of an MCMC sample does not sound absolutely satisfactory since the upper bound in the integral varies for each realisation of the parameter. The use of another estimate would however require a specific loss function, an issue not discussed in the thesis.