115 search results - page 6 / 23 » On Axiomatizing Probabilistic Conditional Independencies in ... |
FLAIRS
2004
13 years 9 months ago
2004
How to compute marginals efficiently is one of major concerned problems in probabilistic reasoning systems. Traditional graphical models do not preserve all conditional independen...
IJAR
2006
13 years 7 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...
AUSAI
2005
Springer
14 years 1 months ago
2005
Springer
Abstract. Programmers employing inference in Bayesian networks typically rely on the inclusion of the model as well as an inference engine into their application. Sophisticated inf...
AI
2008
Springer
13 years 7 months ago
2008
Springer
Graphs provide an excellent framework for interrogating symmetric models of measurement random variables and discovering their implied conditional independence structure. However,...
IJCAI
1997
13 years 9 months ago
1997
Bayesian belief networks have grown to prominence because they provide compact representations of many domains, and there are algorithms to exploit this compactness. The next step...