publication . Preprint . Conference object . 2010

Nested Sampling with Constrained Hamiltonian Monte Carlo

Michael Betancourt; Ali Mohammad-Djafari; Jean-François Bercher; Pierre Bessiére;
Open Access English
  • Published: 02 May 2010
Comment: 15 pages, 4 figures
Persistent Identifiers
free text keywords: Physics - Data Analysis, Statistics and Probability, Importance sampling, Hybrid Monte Carlo, Monte Carlo integration, Slice sampling, Markov chain Monte Carlo, symbols.namesake, symbols, Rejection sampling, Mathematics, Monte Carlo method, Algorithm, Mathematical optimization, Monte Carlo method in statistical physics
Related Organizations

[1] MacKay, D. J. C. (2003) Information Theory, Inference, and Learning Algorithms. Cambridge University Press, New York

[2] Jaynes, E. T. (2003) Probability Theory: The Logic of Science, Cambridge University Press, New York [OpenAIRE]

[3] 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: 395-405. [OpenAIRE]

[4] Sivia, D. S. with Skilling, J. (2006) Data Analysis. Oxford, New York

[5] Feroz, F., Hobson, M. P, Bridges, M. arXiv:0809.3437v1

[6] Brewer, B. J., Partay, L. B., and Csanyi, G. arXiv:0912.2380v1

[7] Bishop, C.M. (2007) Pattern Classification and Machine Learning. Springer, New York

[8] Neal, R. M. MCMC using Hamiltonian, 2010. dynamics, March 5,

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