Zero variance in Markov chain Monte Carlo with an application to credit risk estimation
We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics literature 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.
|Date of creation:||Apr 2008|
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