Introduction to “On the use of Markov chain Monte Carlo methods for the sampling of mixture models” by R. Douc, F. Maire, J. Olsson

Stefano Peluso

doi:10.1007/s11222-014-9532-7

Introduction to “On the use of Markov chain Monte Carlo methods for the sampling of mixture models” by R. Douc, F. Maire, J. Olsson

Stefano Peluso

Università Cattolica del Sacro Cuore

Risultato della ricerca: Contributo in rivista › Articolo

Abstract

We introduce the paper, highlighting contributions and potential fields of application

Lingua originale	Inglese
pagine (da-a)	93-94
Numero di pagine	2
Rivista	Statistics and Computing
Volume	25
DOI	https://doi.org/10.1007/s11222-014-9532-7
Stato di pubblicazione	Pubblicato - 2014

Keywords

Mixture models
Peskun ordering
Pseudo-prior method
inhomogeneous Markov chains

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10.1007/s11222-014-9532-7

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title = "Introduction to “On the use of Markov chain Monte Carlo methods for the sampling of mixture models” by R. Douc, F. Maire, J. Olsson",

abstract = "We introduce the paper, highlighting contributions and potential fields of application",

keywords = "Mixture models, Peskun ordering, Pseudo-prior method, inhomogeneous Markov chains, Mixture models, Peskun ordering, Pseudo-prior method, inhomogeneous Markov chains",

author = "Stefano Peluso",

year = "2014",

doi = "10.1007/s11222-014-9532-7",

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journal = "Statistics and Computing",

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N2 - We introduce the paper, highlighting contributions and potential fields of application

AB - We introduce the paper, highlighting contributions and potential fields of application

KW - Mixture models

KW - Peskun ordering

KW - Pseudo-prior method

KW - inhomogeneous Markov chains

KW - Mixture models

KW - Peskun ordering

KW - Pseudo-prior method

KW - inhomogeneous Markov chains

UR - http://hdl.handle.net/10807/66030

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DO - 10.1007/s11222-014-9532-7

M3 - Article

SN - 0960-3174

VL - 25

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EP - 94

JO - Statistics and Computing

JF - Statistics and Computing

ER -

Introduction to “On the use of Markov chain Monte Carlo methods for the sampling of mixture models” by R. Douc, F. Maire, J. Olsson

Abstract

Keywords

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