Striated Metropolis–Hastings sampler for high-dimensional models
面向高维模型的分层 Metropolis–Hastings 采样器
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…