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» Compiling Bayesian Networks Using Variable Elimination
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UAI
2003
13 years 8 months ago
On Triangulating Dynamic Graphical Models
This paper introduces improved methodology to triangulate dynamic graphical models and dynamic Bayesian networks (DBNs). In this approach, a standard DBN template can be modified...
Jeff A. Bilmes, Chris Bartels
JMLR
2006
118views more  JMLR 2006»
13 years 7 months ago
Learning Factor Graphs in Polynomial Time and Sample Complexity
We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...
Pieter Abbeel, Daphne Koller, Andrew Y. Ng
CORR
2004
Springer
176views Education» more  CORR 2004»
13 years 7 months ago
The First-Order Theory of Sets with Cardinality Constraints is Decidable
Data structures often use an integer variable to keep track of the number of elements they store. An invariant of such data structure is that the value of the integer variable is ...
Viktor Kuncak, Martin C. Rinard
ICML
2010
IEEE
13 years 8 months ago
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Tobias Lang, Marc Toussaint
CGO
2007
IEEE
14 years 1 months ago
Run-Time Support for Optimizations Based on Escape Analysis
The JavaTM programming language does not allow the programmer to influence memory management. An object is usually allocated on the heap and deallocated by the garbage collector ...
Thomas Kotzmann, Hanspeter Mössenböck