| Autor: |
Barrie Stokes, Irene L. Hudson, Frank Tuyl |
| Rok vydání: |
2016 |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings. |
| ISSN: |
0094-243X |
| Popis: |
John Skilling’ s Nested Sampling algorithm [9] is a numerical method for fitting models to data in the Bayesian setting, producing estimates of the Bayesian Evidence Z and Information ℋ as well as posterior samples. A central step in the process is the generation of a new random sample from the (typically uniform) prior distribution subject to the constraint that the new prior sample’s likelihood is greater than a current likelihood threshold. One way to test a generation method - the “outside in” approach - is to incorporate it in a Nested Sampling algorithm and compare the resulting model estimates with known cases. Another way - the “inside out” approach - is to validate the uniformity of prior samples produced by the new method before its incorporation in a Nested Sampling system. Using the “inside out” approach, we show that E T Jaynes’ Entropy Concentration Theorem (ECT) [5, 6] and a Bayes Factor test [7] of a particular type provide sensitive tests of uniformity in irregular 2D regions. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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