SMC 22 coming soon!

The 5th Workshop on Sequential Monte Carlo Methods (SMC 2022) will take place in Madrid on 4-6 May 2022. More precisely on the Leganés campus of Universidad Carlos III de Madrid. Registrations are now open, with very modest registration fees and the list of invited speakers is available on the webpage of the workshop. (The SMC 2020 workshop was cancelled due to the COVID-19 pandemic. An earlier workshop took place at CREST in 2015.)

EM degeneracy

At the MHC 2021 conference today (to which I biked to attend for real!, first time since BayesComp!) I listened to Christophe Biernacki exposing the dangers of EM applied to mixtures in the presence of missing data, namely that the algorithm has a rising probability to reach a degenerate solution, namely a single observation component. Rising in the proportion of missing data. This is not hugely surprising as there is a real (global) mode at this solution. If one observation components are prohibited, they should not be accepted in the EM update. Just as in Bayesian analyses with improper priors, the likelihood should bar single or double  observations components… Which of course makes EM harder to implement. Or not?! MCEM, SEM and Gibbs are obviously straightforward to modify in this case.

Judith Rousseau also gave a fascinating talk on the properties of non-parametric mixtures, from a surprisingly light set of conditions for identifiability to posterior consistency . With an interesting use of several priors simultaneously that is a particular case of the cut models. Namely a correct joint distribution that cannot be a posterior, although this does not impact simulation issues. And a nice trick turning a hidden Markov chain into a fully finite hidden Markov chain as it is sufficient to recover a Bernstein von Mises asymptotic. If inefficient. Sylvain LeCorff presented a pseudo-marginal sequential sampler for smoothing, when the transition densities are replaced by unbiased estimators. With connection with approximate Bayesian computation smoothing. This proves harder than I first imagined because of the backward-sampling operations…

Introduction to Sequential Monte Carlo [book review]

[Warning: Due to many CoI, from Nicolas being a former PhD student of mine, to his being a current colleague at CREST, to Omiros being co-deputy-editor for Biometrika, this review will not be part of my CHANCE book reviews.]

My friends Nicolas Chopin and Omiros Papaspiliopoulos wrote in 2020 An Introduction to Sequential Monte Carlo (Springer) that took several years to achieve and which I find remarkably coherent in its unified presentation. Particles filters and more broadly sequential Monte Carlo have expended considerably in the last 25 years and I find it difficult to keep track of the main advances given the expansive and heterogeneous literature. The book is also quite careful in its mathematical treatment of the concepts and, while the Feynman-Kac formalism is somewhat scary, it provides a careful introduction to the sampling techniques relating to state-space models and to their asymptotic validation. As an introduction it does not go to the same depths as Pierre Del Moral’s 2004 book or our 2005 book (Cappé et al.). But it also proposes a unified treatment of the most recent developments, including SMC² and ABC-SMC. There is even a chapter on sequential quasi-Monte Carlo, naturally connected to Mathieu Gerber’s and Nicolas Chopin’s 2015 Read Paper. Another significant feature is the articulation of the practical part around a massive Python package called particles [what else?!]. While the book is intended as a textbook, and has been used as such at ENSAE and in other places, there are only a few exercises per chapter and they are not necessarily manageable (as Exercise 7.1, the unique exercise for the very short Chapter 7.) The style is highly pedagogical, take for instance Chapter 10 on the various particle filters, with a detailed and separate analysis of the input, algorithm, and output of each of these. Examples are only strategically used when comparing methods or illustrating convergence. While the MCMC chapter (Chapter 15) is surprisingly small, it is actually an introducing of the massive chapter on particle MCMC (and a teaser for an incoming Papaspiloulos, Roberts and Tweedie, a slow-cooking dish that has now been baking for quite a while!).

Bernoulli race particle filters

Sebastian Schmon, Arnaud Doucet and George Deligiannidis have recently arXived an AISTATS paper with the above nice title. The motivation for the extension is facing intractable particle weights for state space models, as for instance in discretised diffusions.  In most cases, actually, the weight associated with the optimal forward proposal involves an intractable integral which is the predictive of the current observed variate given the past hidden states. And in some cases, there exist unbiased and non-negative estimators of the targets,  which can thus be substituted, volens nolens,  to the original filter. As in many pseudo-marginal derivations, this new algorithm can be interpreted as targeting an augmented distribution that involves the auxiliary random variates behind the unbiased estimators of the particle weights. A worthwhile remark since it allows for the preservation of the original target as in (8) provided the auxiliary random variates are simulated from the right conditionals. (At least ideally as I have no clue when this is feasible.)

“if Bernoulli resampling is per-formed, the variance for any Monte Carlo estimate will be the same as if the true weights were known and one applies standard multinomial resampling.”

The Bernoulli race in the title stands for a version of the Bernoulli factory problem, where an intractable and bounded component of the weight can be turned into a probability, for which a Bernoulli draw is available, hence providing a Multinomial sampling with the intractable weights since replacing the exact probability with an estimate does not modify the Bernoulli distribution, amazingly so! Even with intractable normalising constants in particle filters. The practicality of the approach may however be restricted by the possibility of some intractable terms being very small and requiring many rejections for one acceptance, as the number of attempts is a compound geometric. The intractability may add to the time request the drawback of keeping this feature hidden as well. Or force some premature interruption in the settings of a parallel implementation.

IMS workshop [day 5]

The last day of the starting workshop [and my last day in Singapore] was a day of importance [sampling] with talks by Matti Vihola opposing importance sampling and delayed acceptance and particle MCMC, related to several papers of his that I missed. To be continued in the coming weeks at the IMS, which is another reason to regret having to leave that early [as my Parisian semester starts this Monday with an undergrad class at 8:30!]

And then a talk by Joaquín Miguez on stabilizing importance sampling by truncation which reminded me very much of the later work by Andrew Gelman and Aki Vehtari on Pareto smoothed importance sampling, with further operators adapted to sequential settings and the similar drawback that when the importance sampler is poor, i.e., when the simulated points are all very far from the centre of mass, no amount of fudging with the weights will bring the points closer. AMIS made an appearance as a reference method, to be improved by this truncation of the weights, a wee bit surprising as it should bring the large weights of the earlier stages down.

Followed by an almost silent talk by Nick Whiteley, who having lost his voice to the air conditioning whispered his talk in the microphone. Having once faced a lost voice during an introductory lecture to a large undergraduate audience, I could not but completely commiserate for the hardship of the task. Although this made the audience most silent and attentive. His topic was the Viterbi process and its parallelisation, by using a truncated horizon (presenting connection with overdamped Langevin, eg Durmus and Moulines and Dalalyan).

And due to a pressing appointment with my son and his girlfriend [who were traveling through Singapore on that day] for a chili crab dinner on my way to the airport, I missed the final talk by Arnaud Doucet, where he was to reconsider PDMP algorithms without the continuous time layer, a perspective I find most appealing!

Overall, this was a quite diverse and rich [starting] seminar, backed by the superb organisation of the IMS and the smooth living conditions on the NUS campus [once I had mastered the bus routes], which would have made much more sense for me as part of a longer stay, which is actually what happened the previous time I visited the IMS (in 2005), again clashing with my course schedule at home… And as always, I am impressed with the city-state of Singapore, for the highly diverse food scene in particular, but also this [maybe illusory] impression of coexistence between communities. And even though the ecological footprint could certainly be decreased, measures to curb car ownership (with a 150% purchase tax) and use (with congestion charges).