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 one or more particles, which explore a mixture of nested probability distributions, each successive distribution occupying ∼e −1 times the enclosed prior mass of the previous distribution. While NS technically requires independent generation of particles, 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 MCMC-based 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 continuing the run for longer, as in standard MCMC.
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Software (in C++) implementing Diffusive Nested Sampling is available at http://lindor.physics.ucsb.edu/DNest/ under the GNU General Public License.
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Brewer, B.J., Pártay, L.B. & Csányi, G. Diffusive nested sampling. Stat Comput 21, 649–656 (2011). https://doi.org/10.1007/s11222-010-9198-8
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DOI: https://doi.org/10.1007/s11222-010-9198-8
Keywords
- Nested sampling
- Bayesian computation
- Markov chain Monte Carlo