**T**oday I refereed a paper where the authors used ABC to bypass convergence (and implementation) difficulties with their MCMC algorithm. And I am still pondering whether or not this strategy makes sense. If only because ABC needs to handle the same complexity and the same amount of parameters as an MCMC algorithm. While shooting “in the dark” by using the prior or a coarse substitute to the posterior. And I wonder at the relevance of simulating new data when the [true] likelihood value [at the observed data] can be computed. This would sound to me like the relevant and unique “statistics” worth considering…

## Archive for MCMC

## running ABC when the likelihood is available

Posted in Statistics with tags ABC, intractable likelihood, latent variable models, MCMC, MCMC convergence, refereeing on September 19, 2017 by xi'an## Le Chemin [featuring Randal Douc]

Posted in Books, pictures, Statistics, Travel, University life with tags Cambodia, film, French mathematicians, French movies, MCMC, Randal Douc on September 17, 2017 by xi'an**M**y friend and co-author Randal Douc is one of the main actors in the film Le Chemin that came out last week in French cinemas. Taking place in Cambodia and directed by Jeanne Labrune. I have not yet seen the film but will next week as it is scheduled in a nearby cinema (and only six in Paris!)… (Randal was also a main actor in Rithy Panh’s Un barrage contre le Pacifique, as well as the off-voice in the Oscar nominated Rithy Panh’s L’image manquante.) In connection with this new movie, Randal was interviewed in Allociné, the major French website on current movies. With questions about his future film and theatre projects, but none about his on-going maths research!!!

## a conceptual introduction to HMC [reply from the author]

Posted in Statistics with tags blogging, Hamiltonian Monte Carlo, HMC, London, MCMC, Monte Carlo methods, Monte Carlo Statistical Methods, reparameterisation, STAN on September 8, 2017 by xi'an*[Here is the reply on my post from Michael Bétancourt, detailed enough to be promoted from comment to post!]*

As Dan notes this is meant as an introduction for those without a strong mathematical background, hence the focus on concepts rather than theorems! There’s plenty of maths deeper in the references. ;-)

I am not sure I get this sentence. Either it means that an expectation remains invariant under reparameterisation. Or something else and more profound that eludes me. In particular because Michael repeats later (p.25) that the canonical density does not depend on the parameterisation.

What I was trying to get at is that expectations and really all of measure theory are reparameteriztion invariant, but implementations of statistical algorithms that depend on parameterization-dependent representations, namely densities, are not. If your algorithm is sensitive to these parameterization dependencies then you end up with a tuning problem — which parameterization is best? — which makes it harder to utilize the algorithm in practice.

Exact implementations of HMC (i.e. without an integrator) are fully geometric and do not depend on any chosen parameterization, hence the canonical density and more importantly the Hamiltonian being an invariant objects. That said, there are some choices to be made in that construction, and those choices often look like parameter dependencies. See below!

“Every choice of kinetic energy and integration time yields a new Hamiltonian transition that will interact differently with a given target distribution (…) when poorly-chosen, however, the performance can suffer dramatically.”

This is exactly where it’s easy to get confused with what’s invariant and what’s not!

The target density gives rise to a potential energy, and the chosen density over momenta gives rise to a kinetic energy. The two energies transform in opposite ways under a reparameterization so their sum, the Hamiltonian, is invariant.

Really there’s a fully invariant, measure-theoretic construction where you use the target measure directly and add a “cotangent disintegration”.

In practice, however, we often choose a default kinetic energy, i.e. a log density, based on the parameterization of the target parameter space, for example an “identify mass matrix” kinetic energy. In other words, the algorithm itself is invariant but by selecting the algorithmic degrees of freedom based on the parameterization of the target parameter space we induce an implicit parameter dependence.

This all gets more complicated when we introducing the adaptation we use in Stan, which sets the elements of the mass matrix to marginal variances which means that the adapted algorithm is invariant to marginal transformations but not joint ones…

The explanation of the HMC move as a combination of uniform moves along isoclines of fixed energy level and of jumps between energy levels does not seem to translate into practical implementations, at least not as explained in the paper. Simulating directly the energy distribution for a complex target distribution does not seem more feasible than moving up likelihood levels in nested sampling.

Indeed, being able to simulate exactly from the energy distribution, which is equivalent to being able to quantify the density of states in statistical mechanics, is intractable for the same reason that marginal likelihoods are intractable. Which is a shame, because conditioned on those samples HMC could be made embarrassingly parallel!

Instead we draw correlated samples using momenta resamplings between each trajectory. As Dan noted this provides some intuition about Stan (it reduced random walk behavior to one dimension) but also motivates some powerful energy-based diagnostics that immediately indicate when the momentum resampling is limiting performance and we need to improve it by, say, changing the kinetic energy. Or per my previous comment, by keeping the kinetic energy the same but changing the parameterization of the target parameter space. :-)

In the end I cannot but agree with the concluding statement that the geometry of the target distribution holds the key to devising more efficient Monte Carlo methods.

Yes! That’s all I really want statisticians to take away from the paper. :-)

## a conceptual introduction to HMC

Posted in Books, Statistics with tags adiabatic Monte Carlo, differential geometry, Hamiltonian Monte Carlo, HMC, Markov chain Monte Carlo, MCMC, Monte Carlo Statistical Methods, typical set on September 5, 2017 by xi'an

“…it has proven a empirical success on an incredibly diverse set of target distributions encountered in applied problems.”

**I**n January this year (!), Michael Betancourt posted on arXiv a detailed introduction to Hamiltonian Monte Carlo that recouped some talks of his I attended. Like the one in Boston two years ago. I have (re)read through this introduction to include an HMC section in my accelerating MCMC review for WIREs (which writing does not accelerate very much…)

“…this formal construction is often out of reach of theoretical and applied statisticians alike.”

With the relevant provision of Michael being a friend and former colleague at Warwick, I appreciate the paper at least as much as I appreciated the highly intuitive approach to HMC in his talks. It is not very mathematical and does not provide theoretical arguments for the defence of one solution versus another, but it (still) provides engaging reasons for using HMC.

“One way to ensure computational inefficiency is to waste computational resources evaluating the target density and relevant functions in regions of parameter space that have negligible contribution to the desired expectation.”

The paper starts by insisting on the probabilistic importance of *the typical set*, which amounts to a ring for Gaussian-like distributions. Meaning that in high dimensions the mode of the target is not a point that is particularly frequently visited. I find this notion quite compelling and am at the same time [almost] flabbergasted that I have never heard of it before.

“we will consider only a single parameterization for computing expectations, but we must be careful to ensure that any such computation does not depend on the irrelevant details of that parameterization, such as the particular shape of the probability density function.”

I am not sure I get this sentence. Either it means that an expectation remains invariant under reparameterisation. Or something else and more profound that eludes me. In particular because Michael repeats later (p.25) that the canonical density does not depend on the parameterisation.

“Every choice of kinetic energy and integration time yields a new Hamiltonian transition that will interact differently with a given target distribution (…) when poorly-chosen, however, the performance can suffer dramatically.”

When discussing HMC, Michael tends to get a wee bit overboard with superlatives!, although he eventually points out the need for calibration as in the above quote. The explanation of the HMC move as a combination of uniform moves along isoclines of fixed energy level and of jumps between energy levels does not seem to translate into practical implementations, at least not as explained in the paper. Simulating directly the energy distribution for a complex target distribution does not seem more feasible than moving up likelihood levels in nested sampling. (Unless I have forgotten something essential about HMC!) Similarly, when discussing symplectic integrators, the paper intuitively conveys the reason these integrators avoid Euler’s difficulties, even though one has to seek elsewhere for rigorous explanations. In the end I cannot but agree with the concluding statement that the geometry of the target distribution holds the key to devising more efficient Monte Carlo methods.

## unbiased MCMC

Posted in Books, pictures, Statistics, Travel, University life with tags convergence assessment, coupling, coupling from the past, cut distribution, MCMC, MCMSki IV, perfect sampling, renewal process on August 25, 2017 by xi'an**T**wo weeks ago, Pierre Jacob, John O’Leary, and Yves F. Atchadé arXived a paper on unbiased MCMC with coupling. Associating MCMC with unbiasedness is rather challenging since MCMC are rarely producing simulations from the exact target, unless specific tools like renewal can be produced in an efficient manner. (I supported the use of such renewal techniques as early as 1995, but later experiments led me to think renewal control was too rare an occurrence to consider it as a generic convergence assessment method.)

This new paper makes me think I had given up too easily! Here the central idea is coupling of two (MCMC) chains, associated with the debiasing formula used by Glynn and Rhee (2014) and already discussed here. Having the coupled chains meet at some time with probability one implies that the debiasing formula does not need a (random) stopping time. The coupling time is sufficient. Furthermore, several estimators can be derived from the same coupled Markov chain simulations, obtained by starting the averaging at a later time than the first iteration. The average of these (unbiased) averages results into a weighted estimate that weights more the later differences. Although coupling is also at the basis of perfect simulation methods, the analogy between this debiasing technique and perfect sampling is hard to fathom, since the coupling of two chains is not a perfect sampling instant. (Something obvious only in retrospect for me is that the variance of the resulting unbiased estimator is at best the variance of the original MCMC estimator.)

When discussing the implementation of coupling in Metropolis and Gibbs settings, the authors give a simple optimal coupling algorithm I was not aware of. Which is a form of accept-reject also found in perfect sampling I believe. (Renewal based on small sets makes an appearance on page 11.) I did not fully understood the way two random walk Metropolis steps are coupled, in that the normal proposals seem at odds with the boundedness constraints. But coupling is clearly working in this setting, while renewal does not. In toy examples like the (Efron and Morris!) baseball data and the (Gelfand and Smith!) pump failure data, the parameters k and m of the algorithm can be optimised against the variance of the averaged averages. And this approach comes highly useful in the case of the cut distribution, a problem which I became aware of during MCMskiii and on which we are currently working with Pierre and others.

## g-and-k [or -h] distributions

Posted in Statistics with tags ABC, ABC de Sevilla, benchmark, Dennis Prangle, g-and-k distributions, MCMC, numerical derivation, numerical integration, quantile distribution, Wasserstein distance on July 17, 2017 by xi'an**D**ennis Prangle released last week an R package called gk and an associated arXived paper for running inference on the g-and-k and g-and-h quantile distributions. As should be clear from an earlier review on Karian’s and Dudewicz’s book quantile distributions, I am not particularly fond of those distributions which construction seems very artificial to me, as mostly based on the production of a closed-form quantile function. But I agree they provide a neat benchmark for ABC methods, if nothing else. However, as recently pointed out in our Wasserstein paper with Espen Bernton, Pierre Jacob and Mathieu Gerber, and explained in a post of Pierre’s on Statisfaction, the pdf can be easily constructed by numerical means, hence allows for an MCMC resolution, which is also a point made by Dennis in his paper. Using the closed-form derivation of the Normal form of the distribution [i.e., applied to Φ(x)] so that numerical derivation is not necessary.

## slice sampling for Dirichlet mixture process

Posted in Books, Statistics, University life with tags Communications in Statistics, Dirichlet process, Gibbs sampler, intractability, MCMC, Monte Carlo Statistical Methods, normalising constant, slice sampling on June 21, 2017 by xi'an**W**hen working with my PhD student Changye in Dauphine this morning I realised that slice sampling also applies to discrete support distributions and could even be of use in such settings. That it works is (now) straightforward in that the missing variable representation behind the slice sampler also applies to densities defined with respect to a discrete measure. That this is useful transpires from the short paper of Stephen Walker (2007) where we saw this, as Stephen relies on the slice sampler to sample from the Dirichlet mixture model by eliminating the tail problem associated with this distribution. (This paper appeared in Communications in Statistics and it is through Pati & Dunson (2014) taking advantage of this trick that Changye found about its very existence. I may have known about it in an earlier life, but I had clearly forgotten everything!)

While the prior distribution (of the weights) of the Dirichlet mixture process is easy to generate via the stick breaking representation, the posterior distribution is trickier as the weights are multiplied by the values of the sampling distribution (likelihood) at the corresponding parameter values and they cannot be normalised. Introducing a uniform to replace all weights in the mixture with an indicator that the uniform is less than those weights corresponds to a (latent variable) completion [or a demarginalisation as we called this trick in Monte Carlo Statistical Methods]. As elaborated in the paper, the Gibbs steps corresponding to this completion are easy to implement, involving only a finite number of components. Meaning the allocation to a component of the mixture can be operated rather efficiently. Or not when considering that the weights in the Dirichlet mixture are not monotone, hence that a large number of them may need to be computed before picking the next index in the mixture when the uniform draw happens to be quite small.