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Łatuszyński, Krzysztof and Rosenthal, Jeffrey S. (Jeffrey Seth) (2010) Adaptive Gibbs samplers. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. (Working papers).

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Abstract
We consider various versions of adaptive Gibbs and Metropolis withinGibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run, by learning as they go in an attempt to optimise the algorithm. We present a cautionary example of how even a simpleseeming adaptive Gibbs sampler may fail to converge. We then present various positive results guaranteeing convergence of adaptive Gibbs samplers under certain conditions.
Item Type:  Working or Discussion Paper (Working Paper) 

Subjects:  Q Science > QA Mathematics 
Divisions:  Faculty of Science > Statistics 
Library of Congress Subject Headings (LCSH):  Sampling (Statistics) 
Series Name:  Working papers 
Publisher:  University of Warwick. Centre for Research in Statistical Methodology 
Place of Publication:  Coventry 
Date:  2010 
Volume:  Vol.2010 
Number:  No.2 
Number of Pages:  30 
Status:  Not Peer Reviewed 
Access rights to Published version:  Open Access 
Funder:  Natural Sciences and Engineering Research Council of Canada (NSERC) 
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URI:  http://wrap.warwick.ac.uk/id/eprint/35066 
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