averaged acceptance ratios

In another recent arXival, Christophe Andrieu, Sinan Yıldırım, Arnaud Doucet, and Nicolas Chopin study the impact of averaging estimators of acceptance ratios in Metropolis-Hastings algorithms. (It is connected with the earlier arXival rephrasing Metropolis-Hastings in terms of involutions discussed here.)

“… it is possible to improve performance of this algorithm by using a modification where the acceptance ratio r(ξ) is integrated with respect to a subset of the proposed variables.”

This interpretation of the current proposal makes it a form of Rao-Blackwellisation, explicitly mentioned on p.18, where, using a mixture proposal, with an adapted acceptance probability, it depends on the integrated acceptance ratio only. Somewhat magically using this ratio and its inverse with probability ½. And it increases the average Metropolis-Hastings acceptance probability (albeit with a larger number of simulations). Since the ideal averaging is rarely available, the authors implement a Monte Carlo averaging version. With applications to the exchange algorithm and to reversible jump MCMC. The major application is to pseudo-marginal settings with a high complexity (in the number T of terms) and where the authors’ approach does scale efficiently with T. There is even an ABC side to the story as one illustration is made of the ABC approximation to the posterior of an α-stable sample. As an encompassing proposal for handling Metropolis-Hastings environments with latent variables and several versions of the acceptance ratios, this is quite an interesting paper that I think we will study in further detail with our students.

scalable Langevin exact algorithm [Read Paper]

Murray Pollock, Paul Fearnhead, Adam M. Johansen and Gareth O. Roberts (CoI: all with whom I have strong professional and personal connections!) have a Read Paper discussion happening tomorrow [under relaxed lockdown conditions in the UK, except for the absurd quatorzine on all travelers|, but still in a virtual format] that we discussed together [from our respective homes] at Paris Dauphine. And which I already discussed on this blog when it first came out.

Here are quotes I spotted during this virtual Dauphine discussion but we did not come up with enough material to build a significant discussion, although wondering at the potential for solving the O(n) bottleneck, handling doubly intractable cases like the Ising model. And noticing the nice features of the log target being estimable by unbiased estimators. And of using control variates, for once well-justified in a non-trivial environment.

“However, in practice this simple idea is unlikely to work. We can see this most clearly with the rejection sampler, as the probability of survival will decrease exponentially with t—and thus the rejection probability will often be prohibitively large.”

“This can be viewed as a rejection sampler to simulate from μ(x,t), the distribution of the Brownian motion at time  t conditional on its surviving to time t. Any realization that has been killed is ‘rejected’ and a realization that is not killed is a draw from μ(x,t). It is easy to construct an importance sampling version of this rejection sampler.”

noise contrastive estimation

As I was attending Lionel Riou-Durand’s PhD thesis defence in ENSAE-CREST last week, I had a look at his papers (!). The 2018 noise contrastive paper is written with Nicolas Chopin (both authors share the CREST affiliation with me). Which compares Charlie Geyer’s 1994 bypassing the intractable normalising constant problem by virtue of an artificial logit model with additional simulated data from another distribution ψ.

“Geyer (1994) established the asymptotic properties of the MC-MLE estimates under general conditions; in particular that the x’s are realisations of an ergodic process. This is remarkable, given that most of the theory on M-estimation (i.e.estimation obtained by maximising functions) is restricted to iid data.”

Michael Guttman and Aapo Hyvärinen also use additional simulated data in another likelihood of a logistic classifier, called noise contrastive estimation. Both methods replace the unknown ratio of normalising constants with an unbiased estimate based on the additional simulated data. The major and impressive result in this paper [now published in the Electronic Journal of Statistics] is that the noise contrastive estimation approach always enjoys a smaller variance than Geyer’s solution, at an equivalent computational cost when the actual data observations are iid. And the artificial data simulations ergodic. The difference between both estimators is however negligible against the Monte Carlo error (Theorem 2).

This may be a rather naïve question, but I wonder at the choice of the alternative distribution ψ. With a vague notion that it could be optimised in a GANs perspective. A side result of interest in the paper is to provide a minimal (re)parameterisation of the truncated multivariate Gaussian distribution, if only as an exercise for future exams. Truncated multivariate Gaussian for which the normalising constant is of course unknown.

Bayesian inference with intractable normalizing functions

In the latest September issue of JASA I received a few days ago, I spotted a review paper by Jaewoo Park & Murali Haran on intractable normalising constants Z(θ). There have been many proposals for solving this problem as well as several surveys, some conferences and even a book. The current survey focus on MCMC solutions, from auxiliary variable approaches to likelihood approximation algorithms (albeit without ABC entries, even though the 2006 auxiliary variable solutions of Møller et al. et of Murray et al. do simulate pseudo-observations and hence…). This includes the MCMC approximations to auxiliary sampling proposed by Faming Liang and co-authors across several papers. And the paper Yves Atchadé, Nicolas Lartillot and I wrote ten years ago on an adaptive MCMC targeting Z(θ) and using stochastic approximation à la Wang-Landau. Park & Haran stress the relevance of using sufficient statistics in this approach towards fighting computational costs, which makes me wonder if an ABC version could be envisioned.  The paper also includes pseudo-marginal techniques like Russian Roulette (once spelled Roullette) and noisy MCMC as proposed in Alquier et al.  (2016). These methods are compared on three examples: (1) the Ising model, (2) a social network model, the Florentine business dataset used in our original paper, and a larger one where most methods prove too costly, and (3) an attraction-repulsion point process model. In conclusion, an interesting survey, taking care to spell out the calibration requirements and the theoretical validation, if of course depending on the chosen benchmarks.

same simulation, different acceptance

In doubly intractable settings, where the likelihood involves an intractable constant Z(θ), an auxiliary or pseudo- observation x is generated to incorporate strategically located densities in the acceptance probability towards cancelling out the Z(θ)’s. The funny thing is that Møller et al.  (2005) and Murray et al. (2006) both use the same simulations in their auxiliary algorithms, namely θ’~q(θ|θ,y) and x’~f(x|θ’), but return different acceptance probabilities. The former use an artificial target on the pair (θ’,x’) [with a free conditional on x’] while the later uses a pseudo-marginal argument to estimate the missing constant Z(θ) by importance sampling as noticed by Everitt (2012). This apparent paradox is rather common to simulation in that several importance weights can often be constructed for the same importance function. But in the case of doubly intractable distributions, the first approach offers a surprisingly wide variability in the selection of the conditional on x’, which can be absolutely any density g(x|θ,y). And hence could be optimised for maximal acceptance rate. Or maximal effective sample size. In the original paper of Møller et al.  (2005) a plug-in version f(x|θ) was suggested, with θ replaced with a crude estimate. This morning, when discussing both versions with Julien Stoehr, I realised that a geometric average of f(x|θ)’s could be used as well, since the intractable normalising constants would not be an issue [as opposed to an arithmetic or harmonic average]. I [idly] wonder if anything has been done in this direction…