Swendsen–Wang algorithm
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The Swendsen–Wang algorithm is an algorithm for Monte Carlo simulation of the Ising model in which the entire sample is divided into equal-spin clusters. Each cluster is then assigned a new random spin value. Compare the Wolff algorithm.
It has been generalized by Barbu and Zhu (2005) to sampling arbitrary probabilities by viewing it as a Metropolis–Hastings algorithm and computing the acceptance probability of the proposed Monte Carlo move.
References
- Swendsen, R. H., and Wang, J. Nonuniversal critical dynamics in Monte Carlo simulations, Phys. Rev. Lett., 58(2):86–88, 1987.
- Barbu, A., Zhu, S. C. Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities, IEEE Trans Patt. Anal. Mach. Intell., 27(8):1239-1253, 2005.
External links
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