Archive for Monte Carlo Statistical Methods
[Astrostat summer school] fogrise [jatp]
Posted in Kids, Mountains, pictures, Running, Statistics, Travel, University life with tags astrostatistics, Autrans, fog, inversion, MCMC algorithms, Monte Carlo Statistical Methods, ski jump, summer school, sunrise, Vercors on October 11, 2017 by xi'anG²S³18, Breckenridge, CO, June 1730, 2018
Posted in Statistics with tags Breckenridge, Colorado, computational statistics, Edinburgh, Gene Golub, inverse problems, ISBA 2018, MCqMC 2018, Monte Carlo Statistical Methods, poster, Rennes, SIAM, summer school on October 3, 2017 by xi'anJournée algorithmes stochastiques
Posted in Books, pictures, Statistics, University life with tags Jussieu, La Défense, Monte Carlo Statistical Methods, PACBayesian, Paris, PSL, stochastic algorithms, Université Paris Dauphine, Université Pierre et Marie Curie, workshop on September 27, 2017 by xi'anOn December 1, 2017, we will hold a day workshop on stochastic algorithms at Université ParisDauphine, with the following speakers

Rémi Bardenet – CNRS Lille / CRISTAL [10:00]

Nicolas Chopin – ENSAE / CREST [11:00]

Aymeric Dieuleveut – ENS / DI & INRIA [14:00]

Aude Genevay – Dauphine / CEREMADE & INRIA [15:00]

Pierre Monmarché – UPMC / LJLL [16:30]
Details and abstracts of the talks are available on the workshop webpage. Attendance is free, but registration is requested towards planning the morning and afternoon coffee breaks. Looking forward seeing ‘Og’s readers there, at least those in the vicinity!
And while I am targetting Parisians, cryptoBayesians, and nearlyParisians, there is another day workshop on Bayesian and PACBayesian methods on November 16, at Université Pierre et Marie Curie (campus Jussieu), with invited speakers
and a similar request for (free) registration.
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 parameterizationdependent 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 poorlychosen, 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, measuretheoretic 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 energybased 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.”
In 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 Gaussianlike 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 poorlychosen, 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.
MCM 2017 snapshots [#2]
Posted in Books, pictures, Running, Statistics, University life with tags ABC, ABC consistency, abcrf, Art Owen, GNU C library, MCM 2017, Mersenne Twisters, Monte Carlo Statistical Methods, Montréal, R, random forests, ratio of uniform algorithm on July 7, 2017 by xi'anOn the second day of MCM 2017, Emmanuel Gobet (from Polytechnique) gave the morning plenary talk on regression Monte Carlo methods, where he presented several ways of estimating conditional means of rv’s in nested problems where conditioning involves other conditional expectations. While interested in such problems in connection with ABC, I could not see how the techniques developed therein could apply to said problems.
By some of random chance, I ended up attending a hardcore random generation session where the speakers were discussing discrepancies between GNU library generators [I could not understand the target of interest and using MCMC till convergence seemed prone to false positives!], and failed statistical tests of some 64bit Mersenne Twisters, and low discrepancy online subsamples of Uniform samples. Most exciting of all, Josef Leydold gave a talk on ratioofuniforms, on which I spent some time a while ago (till ending up reinventing the wheel!), with highly refined cuts of the original box.
My own 180 slides [for a 50mn talk] somewhat worried my chairman, Art Owen, who kindly enquired the day before at the likelihood I could go through all 184 of them!!! I had appended the ABC convergence slides to an earlier set of slides on ABC with random forests in case of questions about that aspect, although I did not plan to go through those slides [and I mostly covered the 64 other slides] As the talk was in fine more about an inference method than a genuine Monte Carlo technique, plus involved random forests that sounded unfamiliar to many, I did not get many questions from the audience but had several deep discussions with people after the talk. Incidentally, we have just reposted our paper on ABC estimation via random forests, updated the abcrf R package, and submitted it to Peer Community in Evolutionary Biology!
MCM17 snapshots
Posted in Kids, Mountains, pictures, Running, Statistics, Travel, University life with tags adaptive MCMC methods, local scaling, MCM 2017, MetropoliswithinGibbs algorithm, Mont Royal, Monte Carlo Statistical Methods, Montréal, Québec, SaintLaurent, stochastic gradient on July 5, 2017 by xi'anAt MCM2017 today, Radu Craiu presented a talk on adaptive MetropoliswithinGibbs, using a family of proposals for each component of the target and weighting them by jumping distance. And managing the adaptation from the selection rate rather than from the acceptance rate as we did in population Monte Carlo. I find the approach quite interesting in that adaptation and calibration of MetropoliswithinGibbs is quite challenging due to the conditioning, i.e., the optimality of one scale is dependent on the other components. Some of the graphs produced by Radu during the talk showed a form of local adaptivity that seemed promising. This raised a question I could not ask for lack of time, namely that with a large enough collection of proposals, it is unclear why this approach provides a gain compared with particle, sequential or population Monte Carlo algorithms. Indeed, when there are many parallel proposals, clouds of particles can be generated from all proposals in proportion to their appeal and merged together in an importance manner, leading to an easier adaptation. As it went, the notion of local scaling also reflected in Mylène Bédard’s talk on another MetropoliswithinGibbs study of optimal rates. The other interesting sessions I attended were the ones on importance sampling with stochastic gradient optimisation, organised by Ingmar Schuster, and on sequential Monte Carlo, with a divideandconquer resolution through trees by Lindsten et al. I had missed.