An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

Abstract : 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.
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Journal of Computational and Graphical Statistics, Taylor & Francis, 2013, 22 (3), 〈10.1080/10618600.2012.723569〉
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https://hal.inria.fr/hal-00932238
Contributeur : Pierre Del Moral <>
Soumis le : jeudi 16 janvier 2014 - 15:23:06
Dernière modification le : jeudi 11 janvier 2018 - 06:22:36

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Luke Bornn, Pierre E. Jacob, Pierre Del Moral, Arnaud Doucet. An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration. Journal of Computational and Graphical Statistics, Taylor & Francis, 2013, 22 (3), 〈10.1080/10618600.2012.723569〉. 〈hal-00932238〉

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