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Markov Chain Monte Carlo Algorithms for Bayesian Computation, a Survey and Some Generalisation

Wu, Changye; Robert, Christian P. (2020), Markov Chain Monte Carlo Algorithms for Bayesian Computation, a Survey and Some Generalisation, in Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert, Case Studies in Applied Bayesian Data Science, Springer : Berlin Heidelberg, p. 89-119. 10.1007/978-3-030-42553-1_4

Type
Chapitre d'ouvrage
Date
2020
Book title
Case Studies in Applied Bayesian Data Science
Book author
Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert
Publisher
Springer
Series title
Lecture Notes in Mathematics
Published in
Berlin Heidelberg
ISBN
978-3-030-42552-4
Number of pages
420
Pages
89-119
Publication identifier
Metadata
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Author(s)
Wu, Changye
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Robert, Christian P.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
This chapter briefly recalls the major simulation based methods for conducting Bayesian computation, before focusing on partly deterministic Markov processes and a novel modification of the bouncy particle sampler that offers an interesting alternative when dealing with large datasets.