NDLI: Parallel tempering with equi-energy moves

Content Provider	Springer Nature Link
Author	Baragatti, Meïli Grimaud, Agnès Pommeret, Denys
Copyright Year	2012
Abstract	The Equi-Energy Sampler (EES) introduced by Kou et al. (in Ann. Stat. 34(4), 1581–1619, 2006) is based on a population of chains which are updated by local moves and global moves, also called equi-energy jumps. The state space is partitioned into energy rings, and the current state of a chain can jump to a past state of an adjacent chain that has an energy level close to its level. This algorithm has been developed to facilitate global moves between different chains, resulting in a good exploration of the state space by the target chain. This method seems to be more efficient than the classical Parallel Tempering (PT) algorithm. However it is difficult to use in combination with a Gibbs sampler and it necessitates increased storage. We propose an adaptation of this EES that combines PT with the principle of swapping between chains with the same level of energy. This adaptation, that we shall call Parallel Tempering with Equi-Energy Moves (PTEEM), keeps the original idea of the EES method while ensuring good theoretical properties, and practical implementation. Performances of the PTEEM algorithm are compared with those of the EES and of the standard PT algorithms in the context of mixture models, and in a problem of identification of transcription factor binding motifs.
Starting Page	323
Ending Page	339
Page Count	17
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	23
Issue Number	3
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2012-02-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Equi-energy sampler Parallel tempering Population-based Monte Carlo Markov chains Mixture models Binding sites for transcription factors Statistics and Computing/Statistics Programs Artificial Intelligence (incl. Robotics) Statistical Theory and Methods Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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