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» Exploiting Causal Independence Using Weighted Model Counting
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AAAI
2008
13 years 6 months ago
Exploiting Causal Independence Using Weighted Model Counting
Previous studies have demonstrated that encoding a Bayesian network into a SAT-CNF formula and then performing weighted model counting using a backtracking search algorithm can be...
Wei Li 0002, Pascal Poupart, Peter van Beek
PKDD
2010
Springer
148views Data Mining» more  PKDD 2010»
13 years 2 months ago
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models
Abstract. A new method is proposed for compiling causal independencies into Markov logic networks (MLNs). An MLN can be viewed as compactly representing a factorization of a joint ...
Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasa...
SGAI
2004
Springer
13 years 9 months ago
Exploiting Causal Independence in Large Bayesian Networks
The assessment of a probability distribution associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions based on t...
Rasa Jurgelenaite, Peter J. F. Lucas
JAIR
2011
129views more  JAIR 2011»
12 years 11 months ago
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference
Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an ...
Wei Li 0002, Pascal Poupart, Peter van Beek
IJUFKS
2000
111views more  IJUFKS 2000»
13 years 4 months ago
A Factorized Representation of Independence of Causal Influence and Lazy Propagation
Theefficiency of algorithmsfor probabilistic inference in Bayesian networks can be improvedby exploiting independenceof causal influence. Thefactorized representation of independe...
Anders L. Madsen, Bruce D'Ambrosio