Adaptive Equi-Energy Sampler: Convergence and Illustration - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue ACM Transactions on Modeling and Computer Simulation Année : 2013

Adaptive Equi-Energy Sampler: Convergence and Illustration

Résumé

Markov chain Monte Carlo (MCMC) methods allow to sample a distribution known up to a multiplicative constant. Classical MCMC samplers are known to have very poor mixing properties when sampling multimodal distributions. The Equi-Energy sampler is an interacting MCMC sampler proposed by Kou, Zhou and Wong in 2006 to sample difficult multimodal distributions. This algorithm runs several chains at different temperatures in parallel, and allow lower-tempered chains to jump to a state from a higher-tempered chain having an energy `close' to that of the current state. A major drawback of this algorithm is that it depends on many design parameters and thus, requires a significant effort to tune these parameters.

In this paper, we introduce an Adaptive Equi-Energy (AEE) sampler which automates the choice of the selection mecanism when jumping onto a state of the higher-temperature chain. We prove the ergodicity and a strong law of large numbers for AEE, and for the original Equi-Energy sampler as well. Finally, we apply our algorithm to motif sampling in DNA sequences.

Fichier non déposé

Dates et versions

hal-02286347 , version 1 (13-09-2019)

Identifiants

  • HAL Id : hal-02286347 , version 1

Citer

Amandine Schreck, Gersende Fort, Eric Moulines. Adaptive Equi-Energy Sampler: Convergence and Illustration. ACM Transactions on Modeling and Computer Simulation, 2013, 23 (1), pp.27. ⟨hal-02286347⟩
33 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More