parallel adaptive importance sampling
Following Paul Russell’s talk at MCqMC 2016, I took a look at his recently arXived paper. In the plane to Sydney. The pseudo-code representation of the method is identical to our population Monte Carlo algorithm as is the suggestion to approximate the posterior by a mixture, but one novel aspect is to use Reich’s ensemble transportation at the resampling stage, in order to maximise the correlation between the original and the resampled versions of the particle systems. (As in our later versions of PMC, the authors also use as importance denominator the entire mixture rather than conditioning on the selected last-step particle.)
“The output of the resampling algorithm gives us a set of evenly weighted samples that we believe represents the target distribution well”
I disagree with this statement: Reweighting does not improve the quality of the posterior approximation, since it introduces more variability. If the original sample is found missing in its adequation to the target, so is the resampled one. Worse, by producing a sample with equal weights, this step may give a false impression of adequate representation…
Another unclear point in the pape relates to tuning the parameters of the mixture importance sampler. The paper discusses tuning these parameters during a burn-in stage, referring to “due to the constraints on adaptive MCMC algorithms”, which indeed is only pertinent for MCMC algorithms, since importance sampling can be constantly modified while remaining valid. This was a major point for advocating PMC. I am thus unsure what the authors mean by a burn-in period in such a context. Actually, I am also unsure on how they use effective sample size to select the new value of the importance parameter, e.g., the variance β in a random walk mixture: the effective sample size involves this variance implicitly through the realised sample hence changing β means changing the realised sample… This seems too costly to contemplate so I wonder at the way Figure 4.2 is produced.
“A popular approach for adaptive MCMC algorithms is to view the scaling parameter as a random variable which we can sample during the course of the MCMC iterations.”
While this is indeed an attractive notion [that I played with in the early days of adaptive MCMC, with the short-lived notion of cyber-parameters], I do not think it is of much help in optimising an MCMC algorithm, since the scaling parameter need be optimised, resulting into a time-inhomogeneous target. A more appropriate tool is thus stochastic optimisation à la Robbins-Monro, as exemplified in Andrieu and Moulines (2006). The paper however remains unclear as to how the scales are updated (see e.g. Section 4.2).
“Ideally, we would like to use a resampling algorithm which is not prohibitively costly for moderately or large sized ensembles, which preserves the mean of the samples, and which makes it much harder for the new samples to forget a significant region in the density.”
The paper also misses on the developments of the early 2000’s about more sophisticated resampling steps, especially Paul Fearnhead’s contributions (see also Nicolas Chopin’s thesis). There exist valid resampling methods that require a single uniform (0,1) to be drawn, rather than m. The proposed method has a flavour similar to systematic resampling, but I wonder at the validity of returning values that are averages of earlier simulations, since this modifies their distribution into ones with slimmer tails. (And it is parameterisation dependent.) Producing xi with probability pi is not the same as returning the average of the pixi‘s.
This entry was posted on August 30, 2016 at 12:16 am and is filed under Statistics with tags adaptive importance sampling, MCqMC 2016, Monte Carlo simulation and resampling methods for social science, optimal transport, population Monte Carlo, systematic resampling. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.