Approximating Bayes in the 21st Century

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

No 24/21, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain `intractable' statistical problems that challenge exact methods like Markov chain Monte Carlo: for instance, models with unavailable likelihoods, high-dimensional models, and models featuring large data sets. These approximate methods are the subject of this review. The aim is to help new researchers in particular -- and more generally those interested in adopting a Bayesian approach to empirical work -- distinguish between different approximate techniques; understand the sense in which they are approximate; appreciate when and why particular methods are useful; and see the ways in which they can can be combined.

Keywords: Approximate Bayesian inference; intractable Bayesian problems; approximate Bayesian computation; Bayesian synthetic likelihood; variational Bayes; integrated nested Laplace approximation (search for similar items in EconPapers)
Pages: 38
Date: 2021
New Economics Papers: this item is included in nep-cmp, nep-cwa, nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

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