Nested Sampling with Constrained Hamiltonian Monte Carlo

Details Nested Sampling with Constrained Hamiltonian Monte Carlo Nested Sampling with Constrained Hamiltonian Monte Carlo

2010-05-02

arxiv.org/abs/1005.0157v1

Physics

Data Analysis, Statistics and Probability

15 pages, 4 figures

Scientific paper

Nested sampling is a powerful approach to Bayesian inference ultimately limited by the computationally demanding task of sampling from a heavily constrained probability distribution. An effective algorithm in its own right, Hamiltonian Monte Carlo is readily adapted to efficiently sample from any smooth, constrained distribution. Utilizing this constrained Hamiltonian Monte Carlo, I introduce a general implementation of the nested sampling algorithm.

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