01 July 2011
Traditional approaches to probabilistic inference such as loopy belief propagation
and Gibbs sampling typically compute marginals for
the unobserved variables
in a graphical model. However, in many real-world applications the user’s inter-
ests 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.
Venue : Neural Information Processing Systems (NIPS)
External Link: https://people.cs.umass.edu/~mwick/MikeWeb/Publications_files/wick11qam.pdf