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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 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.
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https://hal.archives-ouvertes.fr/hal-00634211
Contributor : Pierre Jacob <>
Submitted on : Thursday, October 20, 2011 - 4:09:00 PM
Last modification on : Friday, December 18, 2020 - 5:30:02 PM

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  • HAL Id : hal-00634211, version 1
  • ARXIV : 1109.3829

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Luke Bornn, Pierre Jacob, Pierre del Moral, Arnaud Doucet. An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration. 2011. ⟨hal-00634211⟩

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