## Archive for qMC

## QMC at CIRM

Posted in Mountains, pictures, Statistics, Travel, University life with tags calanques, CIRM, determinism, Diophantine problems, Jean Morlet Chair, Luminy campus, Marseiile, qMC, quasi-Monte Carlo methods, randomness, research school, Université Aix Marseille on October 21, 2020 by xi'an## dropping a point

Posted in Statistics, University life with tags Illya Sobol, MCMC, MCQMC 2020, Monte Carlo error, pytorch, qMC, quasi-Monte Carlo, scipy, Sobol sequences on September 8, 2020 by xi'an

“A discussion about whether to drop the initial point came up in the plenary tutorial of Fred Hickernell at MCQMC 2020 about QMCPy software for QMC. The issue has been discussed by the pytorch community , and the scipy community, which are both incorporating QMC methods.”

**A**rt Owen recently arXived a paper entitled On dropping the first Sobol’ point in which he examines the impact of a common practice consisting in skipping the first point of a Sobol’ sequence when using quasi-Monte Carlo. By analogy with the burn-in practice for MCMC that aims at eliminating the biais due to the choice of the starting value. Art’s paper shows that by skipping just this one point the rate of convergence of some QMC estimates may drop by a factor, bringing the rate back to Monte Carlo values! As this applies to randomised scrambled Sobol sequences, this is quite amazing. The explanation centers on the suppression leaving one region of the hypercube unexplored, with an O(n⁻¹) error ensuing.

The above picture from the paper makes the case in a most obvious way: the mean squared error is not decreasing at the same rate for the no-drop and one-drop versions, since they are -3/2 and -1, respectively. The paper further “recommends against using roundnumber sample sizes and thinning QMC points.” Conclusion: QMC is not MC!

## ABC by QMC

Posted in Books, Kids, Statistics, University life with tags ABC, ABC-PMC, ABC-SMC, CREST, JCGS, PhD thesis, population genetics, population Monte Carlo, qMC, quasi-Monte Carlo methods, variance reduction on November 5, 2018 by xi'an**A** paper by Alexander Buchholz (CREST) and Nicolas Chopin (CREST) on quasi-Monte Carlo methods for ABC is going to appear in the *Journal of Computational and Graphical Statistics*. I had missed the opportunity when it was posted on arXiv and only became aware of the paper’s contents when I reviewed Alexander’s thesis for the doctoral school. The fact that the parameters are simulated (in ABC) from a prior that is quite generally a standard distribution while the pseudo-observations are simulated from a complex distribution (associated with the intractability of the likelihood function) means that the use of quasi-Monte Carlo sequences is in general only possible for the first part.

The ABC context studied there is close to the original version of ABC rejection scheme [as opposed to SMC and importance versions], the main difference standing with the use of M pseudo-observations instead of one (of the same size as the initial data). This repeated version has been discussed and abandoned in a strict Monte Carlo framework in favor of M=1 as it increases the overall variance, but the paper uses this version to show that the multiplication of pseudo-observations in a quasi-Monte Carlo framework does not increase the variance of the estimator. (Since the variance apparently remains constant when taking into account the generation time of the pseudo-data, we can however dispute the interest of this multiplication, except to produce a constant variance estimator, for some targets, or to be used for convergence assessment.) L The article also covers the bias correction solution of Lee and Latuszyǹski (2014).

Due to the simultaneous presence of pseudo-random and quasi-random sequences in the approximations, the authors use the notion of mixed sequences, for which they extend a one-dimension central limit theorem. The paper focus on the estimation of Z(ε), the normalization constant of the ABC density, ie the predictive probability of accepting a simulation which can be estimated at a speed of O(N⁻¹) where N is the number of QMC simulations, is a wee bit puzzling as I cannot figure the relevance of this constant (function of ε), especially since the result does not seem to generalize directly to other ABC estimators.

A second half of the paper considers a sequential version of ABC, as in ABC-SMC and ABC-PMC, where the proposal distribution is there based on a Normal mixture with a *small* number of components, estimated from the (particle) sample of the previous iteration. Even though efficient techniques for estimating this mixture are available, this innovative step requires a calculation time that should be taken into account in the comparisons. The construction of a decreasing sequence of tolerances ε seems also pushed beyond and below what a sequential approach like that of Del Moral, Doucet and Jasra (2012) would produce, it seems with the justification to always prefer the lower tolerances. This is not necessarily the case, as recent articles by Li and Fearnhead (2018a, 2018b) and ours have shown (Frazier et al., 2018). Overall, since ABC methods are large consumers of simulation, it is interesting to see how the contribution of QMC sequences results in the reduction of variance and to hope to see appropriate packages added for standard distributions. However, since the most consuming part of the algorithm is due to the simulation of the pseudo-data, in most cases, it would seem that the most relevant focus should be on QMC add-ons on this part, which may be feasible for models with a huge number of standard auxiliary variables as for instance in population evolution.

## winning entry at MCqMC’16

Posted in Books, Kids, pictures, Statistics, Travel, University life with tags California, MCqMC 2016, qMC, quasi-random sequences, scientific computing, Stanford University, tee-shirt, uniformity on August 29, 2016 by xi'an**T**he nice logo of MCqMC 2016 was a collection of eight series of QMC dots on the unit (?) cube. The organisers set a competition to identify the principles behind those quasi-random sets and as I had no idea for most of them I entered very random sets unconnected with algorithmia, for which I got an honourable mention and a CD prize (if not the conference staff tee-shirt I was coveting!) Art Owen sent me back my entry, posted below and hopefully (or not!) readable.

## MCMskv #4 [house with a vision]

Posted in Statistics with tags ABC, Bernoulli factory, delayed acceptance, diffusions, Lenzerheide, MCMskv, Metropolis-Hastings algorithm, qMC, quasi-Monte Carlo methods, quasi-random sequences, snow, Switzerland, vanilla Rao-Blackwellisation on January 9, 2016 by xi'an**L**ast day at MCMskv! Not yet exhausted by this exciting conference, but this was the toughest day with one more session and a tutorial by Art Own on quasi Monte-Carlo. (Not even mentioning the night activities that I skipped. Or the ski break that I did not even consider.) Krys Latunszynski started with a plenary on exact methods for discretised diffusions, with a foray in Bernoulli factory problems. Then a neat session on adaptive MCMC methods that contained a talk by Chris Sherlock on delayed acceptance, where the approximation to the target was built by knn trees. (The adaptation was through the construction of the tree by including additional evaluations of the target density. Another paper sitting in my to-read list for too a long while: the exploitation of the observed values of π towards improving an MCMC sampler has always be “obvious” to me even though I could not see any practical way of doing so. )

It was wonderful that Art Owen accepted to deliver a tutorial at MCMskv on quasi-random Monte Carlo. Great tutorial, with a neat coverage of the issues most related to Monte Carlo integration. Since quasi-random sequences have trouble with accept/reject methods, a not-even-half-baked idea that came to me during Art’s tutorial was that the increased computing power granted by qMC could lead to a generic integration of the Metropolis-Hastings step in a Rao-Blackwellised manner. Art mentioned he was hoping that in a near future one could switch between pseudo- and quasi-random in an almost automated manner when running standard platforms like R. This would indeed be great, especially since quasi-random sequences seem to be available at the same cost as their pseudo-random counterpart. During the following qMC session, Art discussed the construction of optimal sequences on sets other than hypercubes (with the surprising feature that projecting optimal sequences from the hypercube does not work). Mathieu Gerber presented the quasi-random simulated annealing algorithm he developed with Luke Bornn that I briefly discussed a while ago. Or thought I did as I cannot trace a post on that paper! While the fact that annealing also works with quasi-random sequences is not astounding, the gain over random sequences shown on two examples is clear. The session also had a talk by Lester Mckey who relies Stein’s discrepancy to measure the value of an approximation to the true target. This was quite novel, with a surprising connection to Chris Oates’ talk and the use of score-based control variates, if used in a dual approach.

Another great session was the noisy MCMC one organised by Paul Jenkins (Warwick), with again a coherent presentation of views on the quality or lack thereof of noisy (or inexact) versions, with an update from Richard Everitt on inexact MCMC, Felipe Medina Aguayo (Warwick) on sufficient conditions for noisy versions to converge (and counterexamples), Jere Koskela (Warwick) on a pseudo-likelihood approach to the highly complex Kingman’s coalescent model in population genetics (of ABC fame!), and Rémi Bardenet on the tall data approximations techniques discussed in a recent post. Having seen or read most of those results previously did not diminish the appeal of the session.