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Betancourt, M. J. (2010)
Languages: English
Types: Preprint
Subjects: Physics - Data Analysis, Statistics and Probability
Identifiers:doi:10.1063/1.3573613
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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