Acceleration Methods for Monte Carlo Integration in Bayesian Inference
Mar 31 1988
Methods for the acceleration of Monte Carlo integration with n replications in a sample of size T are investigated. A general procedure for combining antithetic variation and grid methods with Monte Carlo methods is proposed and it is shown that the numerical accuracy of these hybrid methods can be evaluated routinely. The derivation indicates the characteristics of applications in which acceleration is likely to be most beneficial. This is confirmed in a worked example, in which these acceleration methods reduce the computation time required to achieve a given degree of numerical accuracy by several orders of magnitude.