Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation

We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of “live points” varies to allocate samples more efficiently. In empirical tests the new method significantly improves calculation accuracy compared to standard nested sampling with the same n...

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Vydáno v:Statistics and computing Ročník 29; číslo 5; s. 891 - 913
Hlavní autoři: Higson, Edward, Handley, Will, Hobson, Michael, Lasenby, Anthony
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 11.09.2019
Springer Nature B.V
Témata:
Accuracy
Algorithms
Artificial Intelligence
Mathematical analysis
Mathematics and Statistics
Parameter estimation
Probability and Statistics in Computer Science
Sampling
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Test procedures
Parameter estimation
Bayesian computation
Nested sampling
Bayesian evidence
ISSN:0960-3174, 1573-1375
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Shrnutí:We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of “live points” varies to allocate samples more efficiently. In empirical tests the new method significantly improves calculation accuracy compared to standard nested sampling with the same number of samples; this increase in accuracy is equivalent to speeding up the computation by factors of up to ∼ 72 for parameter estimation and ∼ 7 for evidence calculations. We also show that the accuracy of both parameter estimation and evidence calculations can be improved simultaneously. In addition, unlike in standard nested sampling, more accurate results can be obtained by continuing the calculation for longer. Popular standard nested sampling implementations can be easily adapted to perform dynamic nested sampling, and several dynamic nested sampling software packages are now publicly available.
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ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-018-9844-0