The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo. Issue 3 (23rd March 2018)

• Record Type: Journal Article
• Authors: Betancourt, Michael
• Other Names: Enßlin Torsten A. guestEditor.; Jasche Jens guestEditor.; Skilling John guestEditor.
• Is Part Of: Annalen der Physik. Volume 531:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-03-23
• Subjects: Hamiltonian Monte Carlo -- Markov chain Monte Carlo -- molecular dynamics -- Monte Carlo; Physics -- Periodicals; Chemistry -- Periodicals; 530.05
• DOI: 10.1002/andp.201700214
• Languages: English
• ISSNs: 0003-3804

Abstract

Abstract: From its inception in the 1950s to the modern frontiers of applied statistics, Markov chain Monte Carlo has been one of the most ubiquitous and successful methods in statistical computing. The development of the method in that time has been fueled by not only increasingly difficult problems but also novel techniques adopted from physics. Here, the history of Markov chain Monte Carlo is reviewed from its inception with the Metropolis method to the contemporary state‐of‐the‐art in Hamiltonian Monte Carlo, focusing on the evolving interplay between the statistical and physical perspectives of the method. Abstract : The history of Markov chain Monte Carlo from its inception with the Metropolis method to the contemporary state‐of‐the‐art in Hamiltonian Monte Carlo is reviewed. Focus is given to the evolving interplay between the statistical and physical perspectives of the method.