文章基本信息

标题：A brief tutorial on transformation based Markov Chain Monte Carlo and optimal scaling of the additive transformation
本地全文：下载
作者：Kushal Kr. Dey ; Sourabh Bhattacharya
期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2017
卷号：31
期号：3
页码：569-617
DOI：10.1214/16-BJPS325
语种：English
出版社：Brazilian Statistical Association
摘要：We consider the recently introduced Transformation-based Markov Chain Monte Carlo (TMCMC) (Stat. Methodol. 16 (2014) 100–116), a methodology that is designed to update all the parameters simultaneously using some simple deterministic transformation of a one-dimensional random variable drawn from some arbitrary distribution on a relevant support. The additive transformation based TMCMC is similar in spirit to random walk Metropolis, except the fact that unlike the latter, additive TMCMC uses a single draw from a one-dimensional proposal distribution to update the high-dimensional parameter. In this paper, we first provide a brief tutorial on TMCMC, exploring its connections and contrasts with various available MCMC methods.