**F**or the third time within a year, I have been stuck in an airport hotel by missing a connection! This time on my way to Calgary, thanks to fog over Paris and Amsterdam. And to Air France refusing to switch me to an earlier flight from Paris. Not as strictly stuck as in Delhi, as I could get outside in a sort of no man’s land between runways and expressways, or even reach downtown Amsterdam by public transportation, but with 24 hours to wait for the next flight. The most frustrating part is missing the ice-climbing day I had organised in Banff…

## Archive for Amsterdam

## The Terminal [#2]

Posted in Mountains, pictures, Travel with tags Air France, Amsterdam, Banff, Canada, Delhi, ice climbing, Schiphol, The Terminal on February 19, 2017 by xi'an## automatic variational ABC

Posted in pictures, Statistics with tags ABC, Amsterdam, beta distribution, bias, Kumaraswamy distribution, likelihood function estimator, likelihood-free methods, pseudo-random generator, qbeta, R, variational Bayes methods on July 8, 2016 by xi'an

“Stochastic Variational inference is an appealing alternative to the inefficient sampling approaches commonly used in ABC.”

Moreno et al. [including Ted Meeds and Max Welling] recently arXived a paper merging variational inference and ABC. The argument for turning variational is computational speedup. The traditional (in variational inference) divergence decomposition of the log-marginal likelihood is replaced by an ABC version, parameterised in terms of intrinsic generators (i.e., generators that do not depend on cyber-parameters, like the U(0,1) or the N(0,1) generators). Or simulation code in the authors’ terms. Which leads to the automatic aspect of the approach. In the paper the derivation of the gradient is indeed automated.

“One issue is that even assuming that the ABC likelihood is an unbiased estimator of the true likelihood (which it is not), taking the log introduces a bias, so that we now have a biased estimate of the lower bound and thus biased gradients.”

I wonder how much of an issue this is, since we consider the variational lower bound. To be optimised in terms of the parameters of the variational posterior. Indeed, the endpoint of the analysis is to provide an optimal variational approximation, which remains an approximation whether or not the likelihood estimator is unbiased. A more “severe” limitation may be in the inversion constraint, since it seems to eliminate Beta or Gamma distributions. (Even though calling qbeta(runif(1),a,b) definitely is achievable… And not rejected by a Kolmogorov-Smirnov test.)

Incidentally, I discovered through the paper the existence of the Kumaraswamy distribution, which main appeal seems to be the ability to produce a closed-form quantile function, while bearing some resemblance with the Beta distribution. (Another arXival by Baltasar Trancón y Widemann studies some connections between those, but does not tell how to select the parameters to optimise the similarity.)

## data challenge in Sardinia

Posted in Books, Kids, R, Statistics, Travel, University life with tags Amsterdam, BayesComp, booking.com, data challenge, hackathon, ISBA 2016, Sardinia on June 9, 2016 by xi'an**I**n what I hope is the first occurrence of a new part of ISBA conferences, Booking.com is launching a data challenge at ISBA 2016 next week. The prize being a trip to take part in their monthly hackathon. In Amsterdam. It would be terrific if our Bayesian conferences, including BayesComp, could gather enough data and sponsors to host an hackathon on site! (I was tempted to hold such a challenge for our estimating constants workshop last month, but Iain Murray pointed out to me the obvious difficulties of organising it from scratch…) Details will be available during the conference.

## R typos

Posted in Books, Kids, R, Statistics, Travel, University life with tags Amsterdam, Bayesian Analysis, MCMskv, Metropolis-Hastings algorithm, mixtures, Monte Carlo Statistical Methods, R, random walk, testing as mixture estimation on January 27, 2016 by xi'an**A**t MCMskv, Alexander Ly (from Amsterdam) pointed out to me some R programming mistakes I made in the introduction to Metropolis-Hastings algorithms I wrote a few months ago for the Wiley on-line encyclopedia! While the outcome (Monte Carlo posterior) of the corrected version is moderately changed this is nonetheless embarrassing! The example (if not the R code) was a mixture of a Poisson and a Geometric distributions borrowed from our testing as mixture paper. Among other things, I used a flat prior on the mixture weights instead of a Beta(1/2,1/2) prior *and* a simple log-normal random walk on the mean parameter instead of a more elaborate second order expansion discussed in the text. And I also inverted the probabilities of success and failure for the Geometric density. The new version is now available on arXiv, and hopefully soon on the Wiley site, but one (the?) fact worth mentioning here is that the (right) corrections in the R code first led to overflows, because I was using the Beta random walk Be(εp,ε(1-p)) which major drawback I discussed here a few months ago. With the drag that nearly zero or one values of the weight parameter produced infinite values of the density… Adding 1 (or 1/2) to each parameter of the Beta proposal solved the problem. And led to a posterior on the weight still concentrating on the correct corner of the unit interval. In any case, a big thank you to Alexander for testing the R code and spotting out the several mistakes…