Hybrid Monte Carlo

In mathematics and physics, the hybrid Monte Carlo algorithm, also known as Hamiltonian Monte Carlo, is a Markov chain Monte Carlo method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (i.e., to generate a histogram), or to compute an integral (such as an expected value).

It differs from the Metropolis–Hastings algorithm by reducing the correlation between successive sampled states by using a Hamiltonian evolution between states and additionally by targeting states with a higher acceptance criteria than the observed probability distribution. This causes it to converge more quickly to the absolute probability distribution. It was devised by Simon Duane, A.D. Kennedy, Brian Pendleton and Duncan Roweth in 1987.[1]

See also

Notes

  1. Duane, Simon; A.D. Kennedy, Brian J. Pendleton, and Duncan, Roweth (3 September 1987). "Hybrid Monte Carlo". Physics Letters B 195 (2): 216–222. doi:10.1016/0370-2693(87)91197-X. Retrieved 21 June 2011. Cite uses deprecated parameter |coauthors= (help)

References

This article is issued from Wikipedia - version of the Sunday, January 18, 2015. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.