**T**he scheduled program for ISBA 2020 is now on-line. And full of exciting sessions, many with computational focus. With dear hopes that the nCo-2019 epidemics will have abated by then (and not solely for the sake of the conference, most obviously!). While early registration ends by 15 April, the deadline for junior travel support ends up this month. And so does the deadline for contributions.

## Archive for variational Bayes methods

## ISBA2020 program

Posted in Kids, Statistics, Travel, University life with tags approximate Bayesian inference, Bayesian computing, Bayesian statistics, China, conference, coronavirus epidemcs, high dimensions, ISBA 2020, Kunming, nCo-2019, program, variational Bayes methods, Yunnan on January 29, 2020 by xi'an## 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**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.

## AABI9 tidbits [& misbits]

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags British Columbia, decompression, generative model, image rendering, Langevin MCMC algorithm, maximum entropy, Monte Carlo methods, NeurIPS 2019, optimisation, Python, round table, stochastic gradient, Vancouver, variational Bayes methods on December 10, 2019 by xi'an**T**oday’s Advances in Approximate Bayesian Inference symposium, organised by Thang Bui, Adji Bousso Dieng, Dawen Liang, Francisco Ruiz, and Cheng Zhang, took place in front of Vancouver Harbour (and the tentalising ski slope at the back) and saw more than 400 participants, drifting away from the earlier versions which had a stronger dose of ABC and much fewer participants. There were students’ talks in a fair proportion, as well (and a massive number of posters). As of below, I took some notes during some of the talks with no pretense at exhaustivity, objectivity or accuracy. (This is a blog post, remember?!) Overall I found the day exciting (to the point I did not suffer at all from the usal naps consecutive to very short nights!) and engaging, with a lot of notions and methods I had never heard about. (Which shows how much I know nothing!)

The first talk was by Michalis Titsias, *Gradient-based Adaptive Markov Chain Monte Carlo* (jointly with Petros Dellaportas) involving as its objective function the multiplication of the variance of the move and of the acceptance probability, with a proposed adaptive version merging gradients, variational Bayes, neurons, and two levels of calibration parameters. The method advocates using this construction in a burnin phase rather than continuously, hence does not require advanced Markov tools for convergence assessment. (I found myself less excited by adaptation than earlier, maybe because it seems like switching one convergence problem for another, with additional design choices to be made.)The second talk was by Jakub Swiatkowsk, *The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks*, involving mean field approximation in variational inference (loads of VI at this symposium!), meaning *de facto* searching for a MAP estimator, and reminding me of older factor analysis and other *analyse de données* projection methods, except it also involved neural networks (what else at NeurIPS?!)The third talk was by Michael Gutmann, *Robust Optimisation Monte Carlo*, (OMC) for implicit data generated models (Diggle & Graton, 1982), an ABC talk at last!, using a formalisation through the functional representation of the generative process and involving derivatives of the summary statistic against parameter, in that sense, with the (Bayesian) random nature of the parameter sample only induced by the (frequentist) randomness in the generative transform since a new parameter “realisation” is obtained there as the one providing minimal distance between data and pseudo-data, with no uncertainty or impact of the prior. The Jacobian of this summary transform (and once again a neural network is used to construct the summary) appears in the importance weight, leading to OMC being unstable, beyond failing to reproduce the variability expressed by the regular posterior or even the ABC posterior. It took me a while to wonder `where is Wally?!’ (the prior) as it only appears in the importance weight.

The fourth talk was by Sergey Levine, *Reinforcement Learning, Optimal , Control, and Probabilistic Inference*, back to Kullback-Leibler as the objective function, with linkage to optimal control (with distributions as actions?), plus again variational inference, producing an approximation in sequential settings. This sounded like a type of return of the MaxEnt prior, but the talk pace was so intense that I could not follow where the innovations stood.

The fifth talk was by Iuliia Molchanova, on *Structured Semi-Implicit Variational Inference*, from BAyesgroup.ru (I did not know of a Bayesian group in Russia!, as I was under the impression that Bayesian statistics were under-represented there, but apparently the situation is quite different in machine learning.) The talk brought an interesting concept of semi-implicit variational inference, exploiting some form of latent variables as far as I can understand, using mixtures of Gaussians.

The sixth talk was by Rianne van den Berg, *Normalizing Flows for Discrete Data,* and amounted to covering three papers also discussed in NeurIPS 2019 proper, which I found somewhat of a suboptimal approach to an invited talk, as it turned into a teaser for following talks or posters. But the teasers it contained were quite interesting as they covered normalising flows as integer valued controlled changes of variables using neural networks about which I had just became aware during the poster session, in connection with papers of Papamakarios et al., which I need to soon read.

The seventh talk was by Matthew Hoffman: *Langevin Dynamics as Nonparametric Variational Inference*, and sounded most interesting, both from title and later reports, as it was bridging Langevin with VI, but I alas missed it for being “stuck” in a tea-house ceremony that lasted much longer than expected. (More later on that side issue!)

After the second poster session (with a highly original proposal by Radford Neal towards creating non-reversibility at the level of the uniform generator rather than later on), I thus only attended Emily Fox’s *Stochastic Gradient MCMC for Sequential Data Sources, *which superbly reviewed (in connection with a sequence of papers, including a recent one by Aicher et al.) error rate and convergence properties of stochastic gradient estimator methods there. Another paper I need to soon read!

The one before last speaker, Roman Novak, exposed a Python library about infinite neural networks, for which I had no direct connection (and talks I have always difficulties about libraries, even without a four hour sleep night) and the symposium concluded with a mild round-table. Mild because Frank Wood’s best efforts (and healthy skepticism about round tables!) to initiate controversies, we could not see much to bite from each other’s viewpoint.

## did variational Bayes work?

Posted in Books, Statistics with tags approximate Bayesian inference, asymptotic Bayesian methods, ICML 2018, importance sampling, misspecified model, Pareto distribution, Pareto smoothed importance sampling, posterior predictive, variational Bayes methods, what you get is what you see on May 2, 2019 by xi'an**A**n interesting ICML 2018 paper by Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman I missed last summer on [the fairly important issue of] assessing the quality or lack thereof of a variational Bayes approximation. In the sense of being near enough from the true posterior. The criterion that they propose in this paper relates to the Pareto smoothed importance sampling technique discussed in an earlier post and which I remember discussing with Andrew when he visited CREST a few years ago. The truncation of the importance weights of prior x likelihood / VB approximation avoids infinite variance issues but induces an unknown amount of bias. The resulting diagnostic is based on the estimation of the Pareto order k. If the true value of k is less than ½, the variance of the associated Pareto distribution is finite. The paper suggests to conclude at the worth of the variational approximation when the estimate of k is less than 0.7, based on the empirical assessment of the earlier paper. The paper also contains a remark on the poor performances of the generalisation of this method to marginal settings, that is, when the importance weight is the ratio of the true and variational marginals for a sub-vector of interest. I find the counter-performances somewhat worrying in that Rao-Blackwellisation arguments make me prefer marginal ratios to joint ratios. It may however be due to a poor approximation of the marginal ratio that reflects on the approximation and not on the ratio itself. A second proposal in the paper focus on solely the point estimate returned by the variational Bayes approximation. Testing that the posterior predictive is well-calibrated. This is less appealing, especially when the authors point out the “dissadvantage is that this diagnostic does not cover the case where the observed data is not well represented by the model.” In other words, misspecified situations. This potential misspecification could presumably be tested by comparing the Pareto fit based on the actual data with a Pareto fit based on simulated data. Among other deficiencies, they point that this is “a local diagnostic that will not detect unseen modes”. In other words, *what you get is what you see*.

## 19 dubious ways to compute the marginal likelihood

Posted in Books, Statistics with tags ABC, bridge sampling, harmonic mean estimator, Laplace approximation, MCMC-free, Monte Carlo Statistical Methods, nested sampling, path sampling, power likelihood, stepping stone sampling, variational Bayes methods on December 11, 2018 by xi'an**A** recent arXival on nineteen different [and not necessarily dubious!] ways to approximate the marginal likelihood of a given topology of a philogeny tree that reminded me of our San Antonio survey with Jean-Michel Marin. This includes a version of the Laplace approximation called Laplus (!), accounting for the fact that branch lengths on the tree are positive but may have a MAP at zero. Using a Beta, Gamma, or log-Normal distribution instead of a Normal. For importance sampling, the proposals are derived from either the Laplus (!) approximate distributions or from the variational Bayes solution (based on an Normal product). Harmonic means are still used here despite the obvious danger, along with a defensive version that mixes prior and posterior. Naïve Monte Carlo means simulating from the prior, while bridge sampling seems to use samples from prior and posterior distributions. Path and modified path sampling versions are those proposed in 2008 by Nial Friel and Tony Pettitt (QUT). Stepping stone sampling appears like another version of path sampling, also based on a telescopic product of ratios of normalising constants, the generalised version relying on a normalising reference distribution that need be calibrated. CPO and PPD in the above table are two versions based on posterior predictive density estimates.

When running the comparison between so many contenders, the ground truth is selected as the values returned by MrBayes in a massive MCMC experiment amounting to 7.5 billions generations. For five different datasets. The above picture describes mean square errors for the probabilities of split, over ten replicates [when meaningful], the worst case being naïve Monte Carlo, with nested sampling and harmonic mean solutions close by. Similar assessments proceed from a comparison of Kullback-Leibler divergences. With the (predicatble?) note that “the methods do a better job approximating the marginal likelihood of more probable trees than less probable trees”. And massive variability for the poorest methods:

The comparison above does not account for time and since some methods are deterministic (and fast) there is little to do about this. The stepping steps solutions are very costly, while on the middle range bridge sampling outdoes path sampling. The assessment of nested sampling found in the conclusion is that it “would appear to be an unwise choice for estimating the marginal likelihoods of topologies, as it produces poor approximate posteriors” (p.12). Concluding at the Gamma Laplus approximation being the winner across all categories! (There is no ABC solution studied in this paper as the model likelihood can be computed in this setup, contrary to our own setting.)