Computing Bayes: From Then `Til Now

Gael Martin (), David Frazier () and Christian Robert ()

No 14/22, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first confronted by Bayes in 1763, we highlight the key contributions of: Laplace, Metropolis (and, importantly, his coauthors!), Hammersley and Handscomb, and Hastings, all of which set the foundations for the computational revolution in the late 20th century -- led, primarily, by Markov chain Monte Carlo (MCMC) algorithms. A very short outline of 21st century computational methods -- including pseudo-marginal MCMC, Hamiltonian Monte Carlo, sequential Monte Carlo, and the various `approximate' methods -- completes the paper.

Keywords: History of Bayesian computation; Laplace approximation; Metropolis-Hastings algorithm; importance sampling; Markov chain Monte Carlo; pseudo-marginal methods; Hamiltonian Monte Carlo; sequential Monte Carlo; approximate Bayesian methods (search for similar items in EconPapers)
Pages: 21
Date: 2022
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-his and nep-hpe
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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