Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5095536 | Journal of Econometrics | 2016 | 15 Pages |
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 pools the strengths from the equi-energy and sequential Monte Carlo samplers while avoiding the weaknesses of the standard Metropolis-Hastings algorithm and those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with extremely irregular distributions that contain winding ridges and multiple peaks; and it is robust to how the sampling procedure progresses across stages. The high-dimensional application studied in this paper provides a natural platform for testing any generic sampler.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Daniel F. Waggoner, Hongwei Wu, Tao Zha,