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Fixed Precision MCMC Estimation by Median of Products of Averages

Part of: Algorithms - Computer Science Equilibrium statistical mechanics Markov processes Probabilistic methods, simulation and stochastic differential equations

Published online by Cambridge University Press:  14 July 2016

Wojciech Niemiro  and
Piotr Pokarowski
Wojciech Niemiro*
Affiliation:
Nicolaus Copernicus University
Piotr Pokarowski*
Affiliation:
University of Warsaw
*
∗Work partially supported by the Polish Ministry of Science and Higher Education (grant number N N201 387234).
∗Work partially supported by the Polish Ministry of Science and Higher Education (grant number N N201 387234).
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Abstract

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The standard Markov chain Monte Carlo method of estimating an expected value is to generate a Markov chain which converges to the target distribution and then compute correlated sample averages. In many applications the quantity of interest θ is represented as a product of expected values, θ = µ1 ⋯ µk, and a natural estimator is a product of averages. To increase the confidence level, we can compute a median of independent runs. The goal of this paper is to analyze such an estimator , i.e. an estimator which is a ‘median of products of averages’ (MPA). Sufficient conditions are given for to have fixed relative precision at a given level of confidence, that is, to satisfy . Our main tool is a new bound on the mean-square error, valid also for nonreversible Markov chains on a finite state space.

Keywords

Markov chain Monte Carlorare-event simulationmean-square errorbiasconfidence estimation

MSC classification

Secondary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces) 65C05: Monte Carlo methods 68W20: Randomized algorithms 82B80: Numerical methods (Monte Carlo, series resummation, etc.)
Type
Research Article
Information
Journal of Applied Probability , Volume 46 , Issue 2 , June 2009 , pp. 309 - 329
Copyright
Copyright © Applied Probability Trust 2009 

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