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Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence

dc.contributor.authorMurray, Iain
dc.contributor.authorGhahramani, Zoubin
dc.contributor.authorMacKay, David J. C.
dc.date.accessioned2011-01-31T11:25:46Z
dc.date.available2011-01-31T11:25:46Z
dc.date.issued2006en
dc.identifier.isbn0-9749039-2-2en
dc.identifier.urihttp://homepages.inf.ed.ac.uk/imurray2/pub/06doubly_intractable/en
dc.identifier.urihttp://hdl.handle.net/1842/4703
dc.description.abstractMarkov Chain Monte Carlo (MCMC) algorithms are routinely used to draw samples from distributions with intractable normalization constants. However, standard MCMC algorithms do not apply to doubly-intractable distributions in which there are additional parameter-dependent normalization terms; for example, the posterior over parameters of an undirected graphical model. An ingenious auxiliary-variable scheme (Møller et al., 2004) offers a solution: exact sampling (Propp and Wilson, 1996) is used to sample from a Metropolis–Hastings proposal for which the acceptance probability is tractable. Unfortunately the acceptance probability of these expensive updates can be low. This paper provides a generalization of Møller et al. (2004) and a new MCMC algorithm, which obtains better acceptance probabilities for the same amount of exact sampling, and removes the need to estimate model parameters before sampling begins.en
dc.language.isoenen
dc.titleMCMC for doubly-intractable distributionsen
dc.typeConference Paperen
rps.titleProceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligenceen
dc.extent.noOfPages359-366en
dc.date.updated2011-01-31T11:25:47Z
dc.date.openingDate2006-07-13
dc.date.closingDate2006-07-16


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