scholarly journals Implementation of Nonreversible Metropolis-Hastings algorithms

2018 ◽  
Vol 1087 ◽  
pp. 022004
Author(s):  
Jin Hua Wang ◽  
Bo Yuan
2021 ◽  
Author(s):  
W. John Braun ◽  
Duncan J. Murdoch

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.


2000 ◽  
Vol 49 (4) ◽  
pp. 345-354 ◽  
Author(s):  
Øivind Skare ◽  
Fred Espen Benth ◽  
Arnoldo Frigessi

Statistics ◽  
2007 ◽  
Vol 41 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Yves F. Atchadé ◽  
François Perron

1999 ◽  
Vol 36 (04) ◽  
pp. 1210-1217 ◽  
Author(s):  
G. O. Roberts

This paper considers positive recurrent Markov chains where the probability of remaining in the current state is arbitrarily close to 1. Specifically, conditions are given which ensure the non-existence of central limit theorems for ergodic averages of functionals of the chain. The results are motivated by applications for Metropolis–Hastings algorithms which are constructed in terms of a rejection probability (where a rejection involves remaining at the current state). Two examples for commonly used algorithms are given, for the independence sampler and the Metropolis-adjusted Langevin algorithm. The examples are rather specialized, although, in both cases, the problems which arise are typical of problems commonly occurring for the particular algorithm being used.


2019 ◽  
Vol 2 (2) ◽  
pp. 7
Author(s):  
Yulin Hu ◽  
Yayong Tang

Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions. To solve this problem, one can use the delayed acceptance Metropolis-Hastings algorithm (MHDA) of Christen and Fox (2005). However, the acceptance rate of a proposed value will always be less than in the standard Metropolis-Hastings. We can fix this problem by using the Metropolis-Hastings algorithm with delayed rejection (MHDR) proposed by Tierney and Mira (1999). In this paper, we combine the ideas of MHDA and MHDR to propose a new MH algorithm, named the Metropolis-Hastings algorithm with delayed acceptance and rejection (MHDAR). The new algorithm reduces the computational cost by division of the prior or likelihood functions and increase the acceptance probability by delay rejection of the second stage. We illustrate those accelerating features by a realistic example.


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