Scaling analysis of multiple-try MCMC methods - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Stochastic Processes and their Applications Année : 2012

Scaling analysis of multiple-try MCMC methods

Résumé

Multiple-try methods are extensions of the Metropolis algorithm in which the next state of the Markov chain is selected among a pool of proposals. These techniques have witnessed a recent surge of interest because they lend themselves easily to parallel implementations. We consider extended versions of these methods in which some dependence structure is introduced in the proposal set, extending earlier work by Craiu and Lemieux (2007). We show that the speed of the algorithm increases with the number of candidates in the proposal pool and that the increase in speed is favored by the introduction of dependence among the proposals. A novel version of the hit-and-run algorithm with multiple proposals appears to be very successful.

Dates et versions

hal-00772096 , version 1 (10-01-2013)

Identifiants

Citer

Randal Douc, Mylène Bédard, Éric Moulines. Scaling analysis of multiple-try MCMC methods. Stochastic Processes and their Applications, 2012, 122 (3), pp.758-786. ⟨10.1016/j.spa.2011.11.004⟩. ⟨hal-00772096⟩
93 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More