- Title
- New prior sampling methods for nested sampling - development and testing
- Creator
- Stokes, Barrie; Tuyl, Frank; Hudson, Irene
- Relation
- 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2016). Bayesian Inference and Maximum Entropy Methods in Science and Engineering: Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2016) (Ghent, Belgium 10-15 July, 2016) p. 110003-1-110003-8
- Publisher Link
- http://dx.doi.org/10.1063/1.4985378
- Publisher
- AIP Publishing
- Resource Type
- conference paper
- Date
- 2017
- Description
- Nested Sampling is a powerful algorithm for fitting models to data in the Bayesian setting, introduced by Skilling. The nested sampling algorithm proceeds by carrying out a series of compressive steps, involving successively nested iso-likelihood boundaries, starting with the full prior distribution of the problem parameters. The “central problem” of nested sampling is to draw at each step a sample from the prior distribution whose likelihood is greater than the current likelihood threshold, i.e., a sample falling inside the current likelihood-restricted region. For both flat and informative priors this ultimately requires uniform sampling restricted to the likelihood-restricted region. We present two new methods of carrying out this sampling step, and illustrate their use with the lighthouse problem, a bivariate likelihood used by Gregory and a trivariate Gaussian mixture likelihood. All the algorithm development and testing reported here has been done with Mathematica®.
- Identifier
- http://hdl.handle.net/1959.13/1393250
- Identifier
- uon:33509
- Identifier
- ISBN:9780735415270
- Language
- eng
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