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» A Scheme for Approximating Probabilistic Inference
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CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 4 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
AMAI
2004
Springer
14 years 2 months ago
New Look-Ahead Schemes for Constraint Satisfaction
This paper presents new look-ahead schemes for backtracking search when solving constraint satisfaction problems. The look-ahead schemes compute a heuristic for value ordering and...
Kalev Kask, Rina Dechter, Vibhav Gogate
IJCAI
2001
13 years 10 months ago
Approximate inference for first-order probabilistic languages
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Hanna Pasula, Stuart J. Russell
UAI
1998
13 years 10 months ago
Large Deviation Methods for Approximate Probabilistic Inference
We study two-layer belief networks of binary random variables in which the conditional probabilities Pr childjparents depend monotonically on weighted sums of the parents. In larg...
Michael J. Kearns, Lawrence K. Saul