Quantitative non-geometric convergence bounds for independence samplers
Roberts, Gareth O. and Rosenthal, Jeffrey S. (Jeffrey Seth) (2009) Quantitative non-geometric convergence bounds for independence samplers. Working Paper. University of Warwick. Centre for Research in Statistical Methodology, Coventry.
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Markov chain Monte Carlo (MCMC) algorithms are widely used in statistics, physics, and computer science, to sample from complicated high-dimensional probability distributions. A central question is how quickly the chain converges to the target (stationarity) distribution. In this paper, we consider this question for a particular class of MCMC algorithms, independence samplers (Hastings, 1970; Tierney, 1994).
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Distribution (Probability theory), Markov processes, Monte Carlo method|
|Series Name:||Working papers|
|Publisher:||University of Warwick. Centre for Research in Statistical Methodology|
|Place of Publication:||Coventry|
|Number of Pages:||14|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
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