CMU-CS-08-151Computer Science Department School of Computer Science, Carnegie Mellon University
CMU-CS-08-151
Christopher James Langmead August 2008
Keywords: Graphical models, dynamic Bayesian networks,
inference, learning, decision procedures, propositional SAT, model
checking, temporal logic
This report introduces a novel approach to performing inference and
learning in P(()using
techniques from the field
of Θ|φ)Model Checking. The advantage of our approach is that it enables
scientists to pose and solve inference and learning problems that cannot
be expressed using traditional approaches. The contributions of this
report include: (1) the introduction of the inference and learning problems
over generalized evidence, (2) exact algorithms for solving
these problems for a restricted class of DBNs, and (3) a series of case
studies demonstrating the scalability of our approach. We conclude by
discussing directions for future research.
29 pages
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