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|Title: ||MCMC for doubly-intractable distributions|
|Authors: ||Murray, Iain|
MacKay, David J. C.
|Issue Date: ||2006|
|Journal Title: ||Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence|
|Abstract: ||Markov 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.|
|Appears in Collections:||Informatics Publications|
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