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December 2006 Nested sampling for general Bayesian computation
John Skilling
Bayesian Anal. 1(4): 833-859 (December 2006). DOI: 10.1214/06-BA127

Abstract

Nested sampling estimates directly how the likelihood function relates to prior mass. The evidence (alternatively the marginal likelihood, marginal density of the data, or the prior predictive) is immediately obtained by summation. It is the prime result of the computation, and is accompanied by an estimate of numerical uncertainty. Samples from the posterior distribution are an optional by-product, obtainable for any temperature. The method relies on sampling within a hard constraint on likelihood value, as opposed to the softened likelihood of annealing methods. Progress depends only on the shape of the "nested" contours of likelihood, and not on the likelihood values. This invariance (over monotonic re-labelling) allows the method to deal with a class of phase-change problems which effectively defeat thermal annealing.

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John Skilling. "Nested sampling for general Bayesian computation." Bayesian Anal. 1 (4) 833 - 859, December 2006. https://doi.org/10.1214/06-BA127

Information

Published: December 2006
First available in Project Euclid: 22 June 2012

zbMATH: 1332.62374
MathSciNet: MR2282208
Digital Object Identifier: 10.1214/06-BA127

Keywords: algorithm , annealing , Bayesian computation , evidence , marginal likelihood , Model selection , nest , phase change

Rights: Copyright © 2006 International Society for Bayesian Analysis

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Vol.1 • No. 4 • December 2006
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