标题：An efficient proposal distribution for Metropolis-Hastings using a B-splines technique
作者：Shao, W.;Guo, G.;Meng, F.;Jia, S.
作者机构：[Shao, W] School of Mathematics, Shandong University, Jinan 250100, China;[ Guo, G] Department of Statistics, Shandong University of Technology, Zibo 更多
通讯作者地址：[Shao, W]Shandong Univ, Sch Math, Jinan 250100, Peoples R China.
来源：Computational statistics & data analysis
关键词：B-splines;Efficient proposal distribution;Gibbs sampling;Markov Chain Monte Carlo;Metropolis-Hastings algorithm
摘要：In this paper, we proposed an efficient proposal distribution in the Metropolis-Hastings algorithm using the B-spline proposal Metropolis-Hastings algorithm. This new method can be extended to high-dimensional cases, such as the B-spline proposal in Gibbs sampling and in the Hit-and-Run (BSPHR) algorithm. It improves the proposal distribution in the Metropolis-Hastings algorithm by carrying more information from the target function. The performance of BSPHR was compared with that of other Markov Chain Monte Carlo (MCMC) samplers in simulation and real data examples. Simulation results show that the new method performs significantly better than other MCMC methods.