  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.
