(Enter summary)
Abstract: One of the main shortcomings of Markov chain Monte Carlo samplers is
their inability to mix between modes of the target distribution. In this paper
we show that advance knowledge of the location of these modes can
be incorporated into the MCMC sampler by introducing mode-hopping
moves that satisfy detailed balance. The proposed sampling algorithm
explores local mode structure through local MCMC moves (e.g. diffusion
or Hybrid Monte Carlo) but in addition also represents the relative... (Update)
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BibTeX entry: (Update)
C. Sminchisescu, M. Welling, and G. Hinton. A Mode-Hopping MCMC Sampler. Technical Report CSRG-478, University of Toronto, submitted to Machine Learning Journal, September 2003. 29 http://citeseer.ist.psu.edu/sminchisescu03modehopping.html More
@misc{ sminchisescu03modehopping,
author = "C. Sminchisescu and M. Welling and G. Hinton",
title = "A Mode-Hopping MCMC Sampler",
text = "C. Sminchisescu, M. Welling, and G. Hinton. A Mode-Hopping MCMC Sampler.
Technical Report CSRG-478, University of Toronto, submitted to Machine Learning
Journal, September 2003. 29",
year = "2003",
url = "citeseer.ist.psu.edu/sminchisescu03modehopping.html" }
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