The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models

Abstract

Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the dynamic striated Metropolis- Hastings (DSMH) sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from both the equi-energy sampler and the sequential Monte Carlo sampler by avoiding the weaknesses of the straight Metropolis-Hastings algorithm as well as those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with incredibly irregular distributions that are full of winding ridges and multiple peaks and has the flexibility to take full advantage of parallelism on either desktop computers or clusters. The high-dimensional application studied in this paper provides a natural platform to put to the test generic samplers such as the DSMH sampler.