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• W2133338734 abstract "Traditional approaches to probabilistic inference such as loopy belief propagation and Gibbs sampling typically compute marginals for all the unobserved variables in a graphical model. However, in many real-world applications the user's interests are focused on a subset of the variables, specified by a query. In this case it would be wasteful to uniformly sample, say, one million variables when the query concerns only ten. In this paper we propose a query-specific approach to MCMC that accounts for the query variables and their generalized mutual information with neighboring variables in order to achieve higher computational efficiency. Surprisingly there has been almost no previous work on query-aware MCMC. We demonstrate the success of our approach with positive experimental results on a wide range of graphical models." @default.
• W2133338734 created "2016-06-24" @default.
• W2133338734 creator A5008354502 @default.
• W2133338734 creator A5058514754 @default.
• W2133338734 date "2011-12-12" @default.
• W2133338734 modified "2023-09-24" @default.
• W2133338734 title "Query-Aware MCMC" @default.
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