BORNN, Luke; JACOB, Pierre; DEL MORAL, Pierre; DOUCET, Arnaud

Métadonnées

Licence d’utilisation du document

BORNN, Luke
Department of Statistics [Vancouver] [UBC Statistics]

JACOB, Pierre
Centre de Recherche en Économie et Statistique [CREST]
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]

DEL MORAL, Pierre
Advanced Learning Evolutionary Algorithms [ALEA]

Langue

Document de travail - Pré-publication

Résumé en anglais

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area which we feel deserves much further attention. Towards this aim, this paper proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains -- a feature which both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance is studied in several applications. Through a Bayesian variable selection example, the authors demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm's adaptive proposal to induce mode-jumping is illustrated through a trimodal density and a Bayesian mixture modeling application. Lastly, through a 2D Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models.< Réduire

Origine

Importé de hal

Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251