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Article overview
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Diffusive Nested Sampling | Brendon J. Brewer
; Livia B. Pártay
; Gábor Csányi
; | Date: |
12 Dec 2009 | Abstract: | We introduce a general Monte Carlo method based on Nested Sampling (NS), for
sampling complex probability distributions and estimating the normalising
constant. The method uses a single particle, which explores a mixture of nested
probability distributions, each successive distribution occupying ~ e^{-1}
times the enclosed prior mass of the previous distribution. While classic NS
technically requires independent generation of particles, imperfect Markov
Chain Monte Carlo (MCMC) exploration fits naturally into this technique. We
illustrate the new method on a test problem and find that it can achieve four
times the accuracy of classic Nested Sampling, for the same computational
effort; equivalent to a factor of 16 speedup. An additional benefit is that
more samples and a more accurate evidence value can be obtained simply by
waiting for longer (as in standard MCMC). This is in contrast with classic NS
for which there is no known procedure for merging separate runs when the
exploration is imperfect. | Source: | arXiv, 0912.2380 | Services: | Forum | Review | PDF | Favorites |
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