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Nested Sampling with Constrained Hamiltonian Monte Carlo · Around

2010

Nested Sampling with Constrained Hamiltonian Monte Carlo

Betancourt, M. J.

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Understand

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.

Built on

MacKay, D. J. C. (2003) Information Theory, Inference, and Learning Algorithms . Cambridge University Press, New York
2003
Earlier work this paper cites.
Jaynes, E. T. (2003) Probability Theory: The Logic of Science , Cambridge University Press, New York
2003
Earlier work this paper cites.

Similar

Skilling, J. (2004) Nested Sampling. In Maximum Entropy and Bayesian methods in science and engineering (ed. G. Erickson, J. T. Rychert, C. R. Smith). AIP Conf. Proc., 735
2004
Cited alongside, same era.
Sivia, D. S. with Skilling, J. (2006) Data Analysis . Oxford, New York
2006
Cited alongside, same era.
Feroz, F., Hobson, M. P, Bridges, M. arXiv:0809.3437v1
Original
Cited in the paper.
Brewer, B. J., Partay, L. B., and Csanyi, G. arXiv:0912.2380v1
Original
Cited in the paper.

Then

Bishop, C.M. (2007) Pattern Classification and Machine Learning . Springer, New York
2007
Later among the works it cites.
Neal, R. M. MCMC using Hamiltonian dynamics , http://www.cs.utoronto.ca/~radford/ham-mcmc.abstract.html
Original
2010
Closest in time.

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