Stochastic simulation
Multivariate statistics: a practical approach
Multivariate statistics: a practical approach
An improved acceptance procedure for the hybrid Monte Carlo algorithm
Journal of Computational Physics
A comparison of hybrid strategies for Gibbs sampling in mixed graphical models
Computational Statistics & Data Analysis
Riemann sums for MCMC estimation and convergence monitoring
Statistics and Computing
Approximating Martingales for Variance Reduction in Markov Process Simulation
Mathematics of Operations Research
Bayesian analysis of nonlinear and non-Gaussian state space models via multiple-try sampling methods
Statistics and Computing
Bayesian Core: A Practical Approach to Computational Bayesian Statistics (Springer Texts in Statistics)
Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling
Statistics and Computing
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Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A general purpose variance reduction technique for the MCMC estimator, based on the zero-variance principle introduced in the physics literature, is proposed. Conditions for asymptotic unbiasedness of the zero-variance estimator are derived. A central limit theorem is also proved under regularity conditions. The potential of the idea is illustrated with real applications to probit, logit and GARCH Bayesian models. For all these models, a central limit theorem and unbiasedness for the zero-variance estimator are proved (see the supplementary material available on-line).