Parallel Tempering with Equi-Energy Moves
type: Dataset
description: The Equi-Energy Sampler (EES) introduced by Kou et al [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 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. In this paper we propose an adaptation of this EES that combines PT with the principle of swapping between chains with same levels 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 even if combined with a Gibbs sampler. 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 gene regulatory binding motifs.
issued: 2026-05-26T00:03:43.140779
language: en
modified: 2026-05-26T00:03:43.140784
contact point: Organization
  fn: CCSD
keyword: algorithm-convergence
keyword: binding-sites-for-transcription-factors
keyword: equi-energy-sampler
keyword: infoeu-reposemanticsarticle
keyword: journal-articles
keyword: mixture-models
keyword: parallel-tempering
keyword: population-based-monte-carlo-markov-chains
keyword: statmestatistics-statmethodology-statme
landing page: 0960-3174

Parallel Tempering with Equi-Energy Moves
type: Distribution
format: HTML
issued: 2026-05-26T00:03:43.207777
modified: 2026-05-26T00:03:43.117394
access URL: https://hal.science/hal-00559174

test_moissonnage_selune
type: Agent