Archive for Wang-Landau algorithm

Hamilton confronting intractability (with a li’le help from Metropolis)

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , on April 20, 2012 by xi'an

Last day of a great workshop! I filled more pages of my black notebook (“bloc”) than in the past month!!! This morning started with an Hamiltonian session, Paul Fearnhead presenting recent developments in this area. I liked his coverage very much, esp. because it went away from the physics analogies that always put me off. The idea of getting away from the quadratic form had always seemed natural to me and provided an interesting range for investigations. (I think I rediscovered the topic during the talks, rephrasing almost the same questions as for Girolami’s and Calderhead’s Read Paper!) One thing that still intrigues me is the temporal dimension of the Hamiltonian representation. Indeed, it is “free” in the sense that the simulation problem does not depend on the time the pair (x,p) is moved along the equipotential curve. (In practice, there is a cost in running this move because it needs to be discretised.) But there is no clear target function to set the time “right”. The only scale I can think of is when the pair comes back by its starting point. Which is less silly than it sounds because the discretisation means that all intermediate points can be used, as suggested by Paul via a multiple try scheme. Mark then presented an application of Hamiltonian ideas and schemes to biochemical dynamics, with a supplementary trick of linearisation. Christian Lorenz Müller gave an ambitious grand tour of gradient free optimisation techniques that sounded appealing from a simulation perspective (but would require a few more hours to apprehend!), Geoff Nicholls presented on-going research on approximating Metropolis-Hastings acceptance probabilities in a more general perspective than à la Andrieu-Robert, i.e. accepting some amount of bias, an idea he has explained to me when I visited Oxford. And Pierre Jacob concluded the meeting in the right tone with a pot-pourri of his papers on Wang-Landau. (Once again a talk I had already heard but that helped me make more sense of a complex notion…)

Overall and talk-by-talk, a truly exceptional meeting. Which also set the bar quite high for us to compete at the ICMS meeting on advances in MCM next Monday! Esp. when a portion of the audience in Bristol will appear in Edinburgh as well!an In the meanwhile, I have to rewrite my talk for the seminar in Glasgow tomorrow in order to remove the overlap with my talk there last year… (I note that I have just managed to fly to Scotland with no lost bag, a true achievement!)

Wang, Landau, Markov, and others…

Posted in pictures, Statistics, University life with tags , , , , , , , , , on April 11, 2012 by xi'an

On Thursday, the “Big’MC” seminar welcomes two talks (at 3pm and 4pm, resp., in IHP, Amphi Darboux):

  • Orateur :Pierre Jacob (ENSAE) et Robin Ryder (CEREMADE)
  • Titre : Some aspects of the Wang-Landau algorithm.
  • Résumé : The Wang-Landau algorithm is an adaptive MCMC algorithm which generates a Markov chain designed to move efficiently in the state space, by constantly penalizing already-visited regions. It hence falls into the class of exploratory algorithms, especially when the chosen regions correspond to different levels of density values. We explore two novel aspects of the Wang-Landau algorithm. First, we show that the algorithm reaches the so-called Flat Histogram criterion in finite time, which ensures convergence properties. Second, we examine the effect of using multiple chains, interacting through a common component. That component essentially represents the history of already-visited regions, computed on all the chains. We show numerically the benefit of using parallel chains even if a single processing unit is available, in terms of stabilization of the schedule used in the adaptation process. If time permits, we shall present an ongoing attempt to study theoretically the effect of parallelization using Feynman-Kac semigroups.
  • Références http://arxiv.org/abs/1110.4025 et http://arxiv.org/abs/1109.3829


and

  • Orateur : Nick Whiteley ( Univ. Bristol, UK)
  • Titre  : A particle method for approximating principal eigen-functions and related quantities
  • Résumé : Perron-Frobenius theory treats the existence of a positive eigen-vector associated with the principal eigen-value \lambda_{\star} of a non-negative matrix, say Q. A simple method for approximating this eigen-vector involves computing the iterate \lambda_{\star}^{-n}Q^{(n)}, for large n. In the more general case that Q is a non-negative integral kernel, an extended Perron-Frobenius theory applies, but it is typical that neither the principal eigen-function nor the iterate \lambda_{\star}^{-n}Q^{(n)} can be computed exactly. In this setting we introduce an interacting particle algorithm which yields a numerical approximation of the principal eigen-function and the associated twisted Markov kernel. Some of its theoretical properties will be discussed and applications will be outlined. In particular, the algorithm allows approximation of an optimal importance sampling method for Markov chain rare event estimation.
    Joint work with Nikolas Kantas.
  • Référence : http://arxiv.org/abs/1202.6678

English trip (1)

Posted in Statistics, Travel, University life with tags , , , , , , , , , , , , , on January 25, 2012 by xi'an

Today, I am attending a workshop on the use of graphics processing units in Statistics in Warwick, supported by CRiSM, presenting our recent works with Randal Douc, Pierre Jacob and Murray Smith. (I will use the same slides as in Telecom two months ago, hopefully avoiding the loss of integral and summation signs this time!) Pierre Jacob will talk about Wang-Landau.

Then, tomorrow, I am off to Cambridge to talk about ABC and model choice on Friday afternoon. (Presumably using the same slides as in Provo.)

The (1) in the title is in prevision of a second trip to Oxford next month and another one to Bristol two months after! (The trip to Edinburgh does not count of course, since it is in Scotland!)

advanced Markov chain Monte Carlo methods

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , on December 5, 2011 by xi'an

This book, Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples, by Faming Liang, Chuanhai Liu, and Raymond Carroll, appeared last year and has been sitting on my desk all this time, patiently (?) waiting for a review. When I received it, I took a brief look at it (further than the cool cover!) and then decided I needed more than that to write a useful review! Here are my impressions  on Advanced Markov Chain Monte Carlo Methods after a deeper read. (I have not read any other review in the main statistical journals so far.)

The title, Advanced Markov Chain Monte Carlo Methods, is a clear warning on the level of the book: “advanced”, it certainly is!!! By page 85, the general description of MCMC simulation methods is completed, including perfect sampling and reversible jump MCMC, and the authors engage into a detailed description of highly specialised topics of their choice: Auxiliary variables (Chap. 4), Population-based MCMC (Chap. 5), Dynamic weighting (Chap. 6), Stochastic approximation Monte Carlo (Chap. 7), and MCMC with adaptive proposals (Chap. 8).  The book is clearly inspired by the numerous papers the authors have written in those area, especially Faming Liang. (The uneven distribution of the number of citations per year with peaks in 2000 and 2009 reflects this strong connection.) While the book attempts at broadening the spectrum by including introductory sections, and discussing other papers, it remains nonetheless that this centred focus of the book reduces its potential readership to graduate students and researchers who could directly work on the original papers. I would thus hesitate in teaching my graduate students from this book, given that they only attend a single course on Monte Carlo methods. Continue reading →

the Wang-Landau algorithm reaches the flat histogram in finite time

Posted in R, Statistics, University life with tags , , , , on October 20, 2011 by xi'an

Pierre Jacob and Robin Ryder (from Paris-Dauphine, CREST, and Statisfaction) have just arXived (and submitted to the Annals of Applied Probability) a neat result on the Wang-Landau algorithm. (This algorithm, which modifies the target in a sort of reweighted partioned sampling to achieve faster convergence, has always been perplexing to me.)  They show that some variations of the Wang-Landau algorithm meet the flat histogram criterion in finite time, and, just as importantly that other variations do not reach this criterion. The proof uses elegant Markov chain arguments and I hope the paper makes it through, as there are very few theoretical results on this algorithm. (Pierre also wrote recently a paper with Luke Bornn, Arnaud Doucet, and Pierre Del Moral, on An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration last week, with an associated R package. Not yet on CRAN.)