NDLI: Stopping Tests for Markov Chain Monte-Carlo Methods

Content Provider	Springer Nature Link
Author	Ycart, B.
Copyright Year	2000
Abstract	Markov Chain Monte-Carlo methods produce a random sample of a given distribution by simulating a Markov chain for which the desired distribution is a reversible measure. In order to generate a sample of size n, we propose to run n independent copies of the chain all starting from the same initial state. If n is large enough, the cutoff phenomenon yields a natural stopping rule. Indeed, the access to equilibrium can be detected using empirical estimates for the expectation of a state function. The method is illustrated by the generation of random samples of stable sets on an undirected graph.
Starting Page	23
Ending Page	36
Page Count	14
File Format	PDF
ISSN	13875841
Journal	Methodology and Computing in Applied Probability
Volume Number	2
Issue Number	1
e-ISSN	15737713
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2000-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Life Sciences Statistics Electronic and Computer Engineering Economics general Business/Management Science
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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Stopping Tests for Markov Chain Monte-Carlo Methods