Hamiltonian Monte Carlo: Difference between revisions
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It differs from the [[Metropolis–Hastings algorithm]] by reducing the correlation between successive sampled states by using a [[Hamiltonian mechanics|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.<ref>{{cite journal|last=Duane|first=Simon|coauthors=A.D. Kennedy, Brian J. Pendleton, and Duncan, Roweth|title=Hybrid Monte Carlo|journal=Physics Letters B|date=3 September 1987|volume=195|issue=2|pages=216–222|accessdate=21 June 2011|url=http://www.sciencedirect.com/science/article/pii/037026938791197X|doi=10.1016/0370-2693(87)91197-X }}</ref> |
It differs from the [[Metropolis–Hastings algorithm]] by reducing the correlation between successive sampled states by using a [[Hamiltonian mechanics|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.<ref>{{cite journal|last=Duane|first=Simon|coauthors=A.D. Kennedy, Brian J. Pendleton, and Duncan, Roweth|title=Hybrid Monte Carlo|journal=Physics Letters B|date=3 September 1987|volume=195|issue=2|pages=216–222|accessdate=21 June 2011|url=http://www.sciencedirect.com/science/article/pii/037026938791197X|doi=10.1016/0370-2693(87)91197-X }}</ref> |
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==See also== |
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* [[Dynamic Monte Carlo method]] |
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* [[List of software for Monte Carlo molecular modeling|Software for Monte Carlo molecular modeling]] |
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== Notes == |
== Notes == |
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Revision as of 12:22, 18 January 2015
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
- ^ Duane, Simon (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 journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help)
References
- Neal, Radford M (2011). "MCMC Using Hamiltonian Dynamics" (PDF). In Steve Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng (ed.). Handbook of Markov Chain Monte Carlo. Chapman and Hall/CRC. ISBN 0470177934.
{{cite book}}: CS1 maint: multiple names: editors list (link)