Archive for Institut Henri Poincaré

Kerrie Mengersen’s talk in Paris

Posted in pictures, Statistics with tags , , on May 9, 2012 by xi'an

Kerrie Mengersen (QUT, Brisbane, visiting Paris-Dauphine and CREST) is giving a talk tomorrow at 4:30pm at Institut Henri Poincaré, during the Séminaire Big’MC, following a talk by Meili Baragatti (MISTEA, SupAgro) on parallel tempering ABC (I discussed in this post):

Understanding images: from inferential aims to models to algorithms

In the excitement of working with algorithms, it is sometimes salutary to remind ourselves of their purpose. In this presentation, we consider the analysis of image data and try to match inferential aims, models and computational methods. We describe and compare the approaches in the context of some real case studies in agriculture and environmental monitoring.

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

Xiao-Li Meng’s inception [in Paris]

Posted in Statistics, University life with tags , , , , , on July 27, 2011 by xi'an

Xiao-Li Meng will give a talk in Paris next September 1st, so I advertise it now, before my Parisian readers leave the city for their August retreat. Here is the abstract, explaining the above title:

Statistical Inception for the MCMC Dream: The kick is in the residual (augmentation)!

Xiao-Li Meng

Department of Statistics, Harvard University

The development of MCMC algorithms via data augmentation (DA) or equivalently auxiliary variables has some resemblance to the theme plot of the recent Hollywood hit Inception. We MCMC designers all share essentially the same “3S” dream, that is, to create algorithms that are simple, stable, and speedy. Within that grand dream, however, we have created a rather complex web of tools, with some of them producing very similar algorithms but for unclear reasons, or others that were thought to be of different origins but actually are layered when viewed from a suitable distance. These include conditional augmentation, marginal augmentation, PX-DA, partially non-centering parameterization, sandwiched algorithms, interweaving strategies, ASIS, etc. It turns out that there is a simple statistical insight that can unify essentially all these methods conceptually, and it also provides practical guidelines for their DA constructions. It is the simple concept of regression residuals, which are constructed to be orthogonal to the regression functions. All these methods in one form or another effectively build a residual augmentation. Given a DA distribution f(T, A), where T is our targeted variable (i.e., f(T) is our targeted distribution) and A is the augmented variable, there are two broad classes of residuals depending on whether we regress T on A or A on T. In this talk we will demonstrate how methods like conditional augmentation and partially non-centering parameterization build their residual augmentations by regressing A on T, whereas methods such as marginal augmentation and ASIS effectively use residual augmentations from regressing T on A. For either class, the attempted orthogonality helps to reduce the dependence among MCMC draws, and when the orthogonality leads to true independence as occurring in some special cases, we reach the dream of producing i.i.d. draws. (The talk is based on an upcoming discussion article, especially its rejoinder, Yu and Meng (2011, JCGS) )

The talk will take place at Institut Henri Poincaré, Thursday Sept. 1, at 15:00, as part of the Big’MC seminars.

A seminar I will sadly miss

Posted in Books, Statistics, Travel, University life with tags , , , , , on January 4, 2011 by xi'an

Next Friday, January 7, Jean Claude Saut will give a seminar in Orsay (bâtiment 425, salle 113, 13:30) on “Autour de `A Treatise on Probability’ de John Maynard Keynes  (II)”. I would have liked very much to be there and hear about a mathematician’s views on this book, which are most likely orthogonal to mines… For those in Paris this week there also is a Big’MC seminar on Thursday (IHP, 3pm) with Jean-Louis Foulley on evidence computation (in connection with Ando’s book) and Gilles Celeux (on latent blocks).

Busy Mondays

Posted in Statistics, University life with tags , , , , on September 30, 2010 by xi'an

There is now a combination of no less than three generic statistics seminars alternating on Monday afternoons in Paris:

Fortunately, Pierre Alquier has made a google calendar that summarises where and when to go!