Efficient Markov chain Monte Carlo with incomplete multinomial data

Kwang Woo Ahn; Kung Sik Chan

doi:10.1007/s11222-009-9136-9

Back

Efficient Markov chain Monte Carlo with incomplete multinomial data

Journal article

Peer reviewed

Efficient Markov chain Monte Carlo with incomplete multinomial data

Kwang Woo Ahn and Kung Sik Chan

Statistics and computing, Vol.20(4), pp.447-456

2010

DOI: 10.1007/s11222-009-9136-9

View Online

Abstract

We propose a block Gibbs sampling scheme for incomplete multinomial data. We show that the new approach facilitates maximal blocking, thereby reducing serial dependency and speeding up the convergence of the Gibbs sampler. We compare the efficiency of the new method with the standard, non-block Gibbs sampler via a number of numerical examples.

Details

Title: Subtitle: Efficient Markov chain Monte Carlo with incomplete multinomial data
Creators: Kwang Woo Ahn
Kung Sik Chan
Resource Type: Journal article
Publication Details: Statistics and computing, Vol.20(4), pp.447-456
DOI: 10.1007/s11222-009-9136-9
ISSN: 0960-3174
eISSN: 1573-1375
Language: English
Date published: 2010
Academic Unit: Statistics and Actuarial Science; Radiology
Record Identifier: 9983985975402771

Metrics

16 Record Views

2 Times Cited - Web of Science