An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration
| hal.structure.identifier | Department of Statistics [Vancouver] [UBC Statistics] | |
| dc.contributor.author | BORNN, Luke | |
| hal.structure.identifier | CEntre de REcherches en MAthématiques de la DEcision [CEREMADE] | |
| dc.contributor.author | JACOB, Pierre E. | |
| hal.structure.identifier | Advanced Learning Evolutionary Algorithms [ALEA] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | DEL MORAL, Pierre | |
| hal.structure.identifier | Department of Statistics | |
| dc.contributor.author | DOUCET, Arnaud | |
| dc.date.issued | 2013 | |
| dc.identifier.issn | 1061-8600 | |
| dc.description.abstractEn | 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 that we feel deserves much further attention. Toward this aim, this article 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 that both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance of this new parallel adaptive Wang-Landau algorithm is studied in several applications. Through a Bayesian variable selection example, we demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm's adaptive proposal to induce mode-jumping is illustrated through a Bayesian mixture modeling application. Last, through a two-dimensional Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models. Supplemental materials are available online. | |
| dc.language.iso | en | |
| dc.publisher | Taylor & Francis | |
| dc.title.en | An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1080/10618600.2012.723569 | |
| dc.subject.hal | Mathématiques [math]/Statistiques [math.ST] | |
| dc.subject.hal | Statistiques [stat]/Théorie [stat.TH] | |
| dc.identifier.arxiv | 1109.3829 | |
| bordeaux.journal | Journal of Computational and Graphical Statistics | |
| bordeaux.volume | 22 | |
| bordeaux.issue | 3 | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-00932238 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-00932238v1 | |
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