**A**n ICLR 2019 paper by Neklyudov, Egorov and Vetrov on an optimal choice of the proposal in an independent Metropolis algorithm I discovered via an X validated question. Namely whether or not the expected Metropolis-Hastings acceptance ratio is always one (which it is not when the support of the proposal is restricted). The paper mentions the domination of the Accept-Reject algorithm by the associated independent Metropolis-Hastings algorithm, which has actually been stated in our Monte Carlo Statistical Methods (1999, Lemma 6.3.2) and may prove even older. The authors also note that the expected acceptance probability is equal to one minus the total variation distance between the joint defined as target x Metropolis-Hastings proposal distribution and its time-reversed version. Which seems to suffer from the same difficulty as the one mentioned in the X validated question. Namely that it only holds when the support of the Metropolis-Hastings proposal is at least the support of the target (or else when the support of the joint defined as target x Metropolis-Hastings proposal distribution is somewhat symmetric. Replacing total variation with Kullback-Leibler then leads to a manageable optimisation target if the proposal is a parameterised independent distribution. With a GAN version when the proposal is not explicitly available. I find it rather strange that one still seeks independent proposals for running Metropolis-Hastings algorithms as the result will depend on the family of proposals considered and as performances will deteriorate with dimension (the authors mention a 10% acceptance rate, which sounds quite low). [As an aside, ICLR 2020 will take part in Addis Abeba next April.]

## Archive for importance sampling

## an independent sampler that maximizes the acceptance rate of the MH algorithm

Posted in Books, Kids, Statistics, University life with tags accept-reject algorithm, adaptive Monte Carlo algorithm, Addis Abeba, Bayesian GANs, Ethiopia, ICLR 2019, importance sampling, Kullback-Leibler divergence, Monte Carlo Statistical Methods, optimal acceptance rate, optimisation, reversibility, simulation, total variation on September 3, 2019 by xi'an## unbiased product of expectations

Posted in Books, Statistics, University life with tags ABC, g-and-k distributions, importance sampling, latent variable models, NP-complete problem, permanent of a matrix, pseudo-marginal MCMC, recycling, University of Warwick on August 5, 2019 by xi'an**W**hile I was not involved in any way, or even aware of this research, Anthony Lee, Simone Tiberi, and Giacomo Zanella have an incoming paper in Biometrika, and which was partly written while all three authors were at the University of Warwick. The purpose is to design an efficient manner to approximate the product of n unidimensional expectations (or integrals) all computed against the same reference density. Which is not a real constraint. A neat remark that motivates the method in the paper is that an improved estimator can be connected with the permanent of the n x N matrix A made of the values of the n functions computed at N different simulations from the reference density. And involves N!/ (N-n)! terms rather than N to the power n. Since it is NP-hard to compute, a manageable alternative uses random draws from constrained permutations that are reasonably easy to simulate. Especially since, given that the estimator recycles most of the particles, it requires a much smaller version of N. Essentially N=O(n) with this scenario, instead of O(n²) with the basic Monte Carlo solution, towards a similar variance.

This framework offers many applications in latent variable models, including pseudo-marginal MCMC, of course, but also for ABC since the ABC posterior based on getting each simulated observation close enough from the corresponding actual observation fits this pattern (albeit the dependence on the chosen ordering of the data is an issue that can make the example somewhat artificial).

## improved importance sampling via iterated moment matching

Posted in Statistics with tags curse of dimensionality, finite variance, importance sampling, infinite variance estimators, Pareto smoothed importance sampling on August 1, 2019 by xi'an**T**opi Paananen, Juho Piironen, Paul-Christian Bürkner and Aki Vehtari have recently arXived a work on constructing an adapted importance (sampling) distribution. The beginning is more a review than a new contribution, covering the earlier work by Vehtari, Gelman and Gabri (2017): estimating the Pareto rate for the importance weight distribution helps in assessing whether or not this distribution allows for a (necessary) second moment. In case it does not (seem to), the authors propose an affine transform of the importance distribution, using the earlier sample to match the first two moments of the distribution. Or of the targeted function. Adaptation that is controlled by the same Pareto rate technique, as in the above picture (from the paper). Predicting a natural objection as to the poor performances of the earlier samples, the paper suggests to use robust estimators of these moments, for instance via Pareto smoothing. It also suggests using multiple importance sampling as a way to regularise and robustify the estimates. While I buy the argument of fitting the target moments to achieve a better fit of the importance sampling, I remain unclear as to why an affine transform would change the (poor) tail behaviour of the importance sampler. Hence why it would apply in full generality. An alternative could consist in finding appropriate Box-Cox transforms, although the difficulty would certainly increase with the dimension.

## sampling and imbalanced

Posted in Statistics with tags big data, importance sampling, logistic regression, PDMP, Poisson process, Zig-Zag on June 21, 2019 by xi'an**D**eborshee Sen, Matthias Sachs, Jianfeng Lu and David Dunson have recently arXived a sub-sampling paper for classification (logistic) models where some covariates or some responses are imbalanced. With a PDMP, namely zig-zag, used towards preserving the correct invariant distribution (as already mentioned in an earlier post on the zig-zag zampler and in a recent Annals paper by Joris Bierkens, Paul Fearnhead, and Gareth Roberts (Warwick)). The current paper is thus an improvement on the above. Using (non-uniform) importance sub-sampling across observations and simpler upper bounds for the Poisson process. A rather practical form of Poisson thinning. And proposing unbiased estimates of the sub-sample log-posterior as well as stratified sub-sampling.

I idly wondered if the zig-zag sampler could itself be improved by not switching the bouncing directions at random since directions associated with almost certainly null coefficients should be neglected as much as possible, but the intensity functions associated with the directions do incorporate this feature. Except for requiring computation of the intensities for all directions. This is especially true when facing many covariates.

Thinking of the logistic regression model itself, it is sort of frustrating that something so close to an exponential family causes so many headaches! Formally, it is an exponential family but the normalising constant is rather unwieldy, especially when there are many observations and many covariates. The Polya-Gamma completion is a way around, but it proves highly costly when the dimension is large…

## MCMC importance samplers for intractable likelihoods

Posted in Books, pictures, Statistics with tags ABC, ABC-MCMC, approximate likelihood, arXiv, delayed acceptance, Finland, hidden Markov models, importance sampling, MCMC, PhD thesis, reversibility, University of Jyväskylä on May 3, 2019 by xi'an**J**ordan Franks just posted on arXiv his PhD dissertation at the University of Jyväskylä, where he discuses several of his works:

- M. Vihola, J. Helske, and J. Franks. Importance sampling type estimators based on approximate marginal MCMC. Preprint arXiv:1609.02541v5, 2016.
- J. Franks and M. Vihola. Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance. Preprint arXiv:1706.09873v4, 2017.
- J. Franks, A. Jasra, K. J. H. Law and M. Vihola.Unbiased inference for discretely observed hidden Markov model diffusions. Preprint arXiv:1807.10259v4, 2018.
- M. Vihola and J. Franks. On the use of ABC-MCMC with inflated tolerance and post-correction. Preprint arXiv:1902.00412, 2019

focusing on accelerated approximate MCMC (in the sense of pseudo-marginal MCMC) and delayed acceptance (as in our recently accepted paper). Comparing delayed acceptance with MCMC importance sampling to the advantage of the later. And discussing the choice of the tolerance sequence for ABC-MCMC. (Although I did not get from the thesis itself the target of the improvement discussed.)

## 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*.

## Gibbs clashes with importance sampling

Posted in pictures, Statistics with tags Amsterdam, cross validated, Gibbs sampling, importance sampling, infinite variance estimators, normalising constant on April 11, 2019 by xi'an**I**n an X validated question, an interesting proposal was made: at each (component-wise) step of a Gibbs sampler, replace simulation from the exact full conditional with simulation from an alternate density and weight the resulting simulation with a term made of a product of (a) the previous weight (b) the ratio of the true conditional over the substitute for the new value and (c) the inverse ratio for the earlier value of the same component. Which does not work for several reasons:

- the reweighting is doomed by its very propagation in that it keeps multiplying ratios of expectation one, which means an almost sure chance of degenerating;
- the weights are computed for a previous value that has not been generated from the same proposal and is anyway already properly weighted;
- due to the change in dimension produced by Gibbs, the actual target is the full conditional, which involves an intractable normalising constant;
- there is no guarantee for the weights to have finite variance, esp. when the proposal has thinner tails than the target.

as can be readily checked by a quick simulation experiment. The funny thing is that a proper importance weight can be constructed when envisioning the sequence of Gibbs steps as a Metropolis proposal (in the dimension of the target).