A central limit theorem for adaptive and interacting Markov chains
type: Item
instance of: scholarly article
title: A central limit theorem for adaptive and interacting Markov chains
zbMATH Open document ID: 1303.60020
DOI: 10.3150/12-BEJ493
author: Gersende Fort
author: Eric Moulines
author: P. Priouret
author: P. Vandekerkhove
published in: Bernoulli
publication date: 2014-05-05T00:00:00Z
full work available at URL: https://arxiv.org/abs/1107.2574
full work available at URL: https://projecteuclid.org/euclid.bj/1393593994
review text: The paper studies a general (non-Markovian) simulation framework covering both the adaptive and interacting Markov chains Monte Carlo (MCMC) algorithms. In Section 2, it contains the main theorem which establishes central limit theorems for adaptive and interacting MCMC algorithms. These results are applied in Section 3.2 to the 2-chain interacting tempering algorithm which is a simplified version of the equi-energy sampler.
Mathematics Subject Classification ID: 60F05
Mathematics Subject Classification ID: 60J05
Mathematics Subject Classification ID: 65C05
zbMATH DE Number: 6291810
zbMATH Keywords: interacting Markov chain Monte Carlo algorithms
zbMATH Keywords: cental limit theorems
reviewed by: Oleg K. Zakusilo
MaRDI profile type: Publication
arXiv ID: 1107.2574
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description: scientific article