Bayesian Analysis

Nested sampling for general Bayesian computation

John Skilling

Full-text: Open access

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.

Article information

Source
Bayesian Anal. Volume 1, Number 4 (2006), 833-859.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340370944

Digital Object Identifier
doi:10.1214/06-BA127

Mathematical Reviews number (MathSciNet)
MR2282208

Zentralblatt MATH identifier
1332.62374

Keywords
Bayesian computation evidence marginal likelihood algorithm nest annealing phase change model selection

Citation

Skilling, John. Nested sampling for general Bayesian computation. Bayesian Anal. 1 (2006), no. 4, 833--859. doi:10.1214/06-BA127. https://projecteuclid.org/euclid.ba/1340370944.


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