Computing Bayes: Bayesian Computation from 1763 to the 21st Century
Gael Martin (),
David Frazier () and
Christian Robert ()
No 14/20, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian inference, with the rules of probability used to transform prior probability distributions for all unknowns - models, parameters, latent variables - into posterior distributions, subsequent to the observation of data. Conducting Bayesian inference requires the evaluation of integrals in which these probability distributions appear. Bayesian computation is all about evaluating such integrals in the typical case where no analytical solution exists. This paper takes the reader on a chronological tour of Bayesian computation over the past two and a half centuries. Beginning with the one-dimensional integral first confronted by Bayes in 1763, through to recent problems in which the unknowns number in the millions, we place all computational problems into a common framework, and describe all computational methods using a common notation. The aim is to help new researchers in particular - and more generally those interested in adopting a Bayesian approach to empirical work - make sense of the plethora of computational techniques that are now on offer; understand when and why different methods are useful; and see the links that do exist, between them all.
Keywords: history of Bayesian computation; Laplace approximation; Markov chain Monte Carlo; importance sampling; approximate Bayesian computation; Bayesian synthetic likelihood; variational Bayes; integrated nested Laplace approximation (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-dcm, nep-ecm, nep-gen, nep-hpe and nep-ore
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