A multiple-try Metropolis-Hastings algorithm with tailored proposals

2018/07/05 by Luo, Xin, Tjelmeland, Håkon
#Computation (stat.CO) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1807.01914

Abstract

We present a new multiple-try Metropolis-Hastings algorithm designed to be especially beneficial when a tailored proposal distribution is available. The algorithm is based on a given acyclic graph G, where one of the nodes in G, k say, contains the current state of the Markov chain and the remaining nodes contain proposed states generated by applying the tailored proposal distribution. The Metropolis-Hastings algorithm alternates between two types of updates. The first update type is using the tailored proposal distribution to generate new states in all nodes in G except in node k. The second update type is generating a new value for k, thereby changing the value of the current state. We evaluate the effectiveness of the proposed scheme in an example with previously defined target and proposal distributions.

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