Diffusive nested sampling |
| |
Authors: | Brendon J Brewer Livia B Pártay Gábor Csányi |
| |
Institution: | (1) Department of Geology and Mineral Resources Engineering, Norwegian University of Science and Technology, N-7491 Trondheim, Norway |
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|