2010/05/02 by M. J. Betancourt
Physics and Astronomy · #physics.data-an
paper · pdf · doi:10.1063/1.3573613
published as AIP Conf. Proc. 1305, 165 (2011) · 15 pages, 4 figures
arxiv created 2010/05/02 · arxiv updated 2015/03/02
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.