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AIIA
2003
Springer
13 years 10 months ago
2003
Springer
A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for b...
GECCO
2003
Springer
13 years 10 months ago
2003
Springer
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
IJAR
2006
13 years 5 months ago
2006
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
ECAI
2008
Springer
13 years 7 months ago
2008
Springer
Abstract. Two approaches have been used to perform approximate inference in Bayesian networks for which exact inference is infeasible: employing an approximation algorithm, or appr...
DATE
2007
IEEE
13 years 12 months ago
2007
IEEE
This paper focuses on inductive invariants in unbounded model checking to improve efficiency and scalability. First of all, it introduces optimized techniques to speedup the comp...