Zero variance in Markov chain Monte Carlo with an application to credit risk estimation
AbstractWe propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics lit- erature by Assaraf and Ca arel ( 1999). The potential of the new idea is illustrated with some toy examples and a real application to Bayesian inference for credit risk estimation.
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Bibliographic InfoPaper provided by Department of Economics, University of Insubria in its series Economics and Quantitative Methods with number qf0804.
Length: 23 pages
Date of creation: Apr 2008
Date of revision:
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Markov chain Monte Carlo; Metropolis-Hastings algorithm; Variance re- duction; Zero-Variance principle.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-08-21 (All new papers)
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