Year: 2014
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Glynn, Peter W., Rhee, Chang-han
Institutions: Stanford University, Georgia Institute of Technology
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Samsung
OpenAlex ID: W4299570544