文章基本信息

标题：A brief review of optimal scaling of the main MCMC approaches and optimal scaling of additive TMCMC under non-regular cases
本地全文：下载
作者：Kushal K. Dey ; Sourabh Bhattacharya
期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2019
卷号：33
期号：2
页码：222-266
DOI：10.1214/17-BJPS386
语种：English
出版社：Brazilian Statistical Association
摘要：Transformation based Markov Chain Monte Carlo (TMCMC) was proposed by Dutta and Bhattacharya (Statistical Methodology 16 (2014) 100–116) as an efficient alternative to the Metropolis–Hastings algorithm, especially in high dimensions. The main advantage of this algorithm is that it simultaneously updates all components of a high dimensional parameter using appropriate move types defined by deterministic transformation of a single random variable. This results in reduction in time complexity at each step of the chain and enhances the acceptance rate.