Improving on the Independent Metropolis-Hastings Algorithm
نویسندگان
چکیده
This paper proposes methods to improve Monte Carlo estimates when the Independent MetropolisHastings Algorithm (IMHA) is used. Our rst approach uses a control variate based on the sample generated by the proposal distribution. We derive the variance of our estimator for a xed sample size n and show that, as n tends to in nity, this variance is asymptotically smaller than the one obtained with the IMHA. Our second approach is based on Jensen's inequality. We use a Rao-Blackwellization and exploit the lack of symmetry in the IMHA. An upper bound on the improvements that we can obtain by these methods is derived. AMS Classi cation: 65C40, 60J22, 60J10.
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