NDLI: Exact estimation for Markov chain equilibrium expectations

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Exact estimation for Markov chain equilibrium expectations

Content Provider	Scilit
Author	Glynn, Peter W. Rhee, Chang-Han
Copyright Year	2014
Description	We introduce a new class of Monte Carlo methods, which we call exact estimation algorithms. Such algorithms provide unbiased estimators for equilibrium expectations associated with real-valued functionals defined on a Markov chain. We provide easily implemented algorithms for the class of positive Harris recurrent Markov chains, and for chains that are contracting on average. We further argue that exact estimation in the Markov chain setting provides a significant theoretical relaxation relative to exact simulation methods.
Related Links	https://www.cambridge.org/core/services/aop-cambridge-core/content/view/844D4F8B0872C8516FE8B51B2BAA103B/S0021900200021392a.pdf/div-class-title-exact-estimation-for-markov-chain-equilibrium-expectations-div.pdf
Ending Page	389
Page Count	13
Starting Page	377
ISSN	00219002
e-ISSN	14756072
DOI	10.1017/s0021900200021392
Journal	Journal of applied probability
Issue Number	A
Volume Number	51
Language	English
Publisher	Cambridge University Press (CUP)
Publisher Date	2014-12-01
Access Restriction	Open
Subject Keyword	Journal of applied probability Statistics and Probability Unbiased Estimation Markov Chain Equilibrium Expectation Markov Chain Stationary Expectation Exact Estimation Exact Sampling Exact Simulation Perfect Sampling Perfect Simulation
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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