Nested sampling for general Bayesian computation
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.
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Information theory, inference, and learning algorithms | 2004 | 6,580 |
| Slice sampling | 2003 | 1,367 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidenc… | 2020 | 2,299 |
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Topics
| Gaussian Processes and Bayesian Inference | Computer Science |
| Markov Chains and Monte Carlo Methods | Mathematics |
| Statistical Methods and Bayesian Inference | Mathematics |
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