A multi-point Metropolis scheme with generic weight functions

Abstract

The multi-point Metropolis algorithm is an advanced MCMC technique based on drawing several correlated samples at each step and choosing one of them according to some normalized weights. We propose a variation of this technique where the weight functions are not specified, i.e., the analytic form can be chosen arbitrarily. This has the advantage of greater flexibility in the design of high-performance MCMC samplers. We prove that our method fulfills the balance condition, and provide a numerical simulation. We also give new insight into the functionality of different MCMC algorithms, and the connections between them.

References in corpus (2)

• On the ergodicity properties of some adaptive MCMC algorithms
• Interacting Multiple Try Algorithms with Different Proposal Distributions

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