86 search results - page 5 / 18 » Conditioning Graphs: Practical Structures for Inference in B... |
IPMU
1992
Springer
13 years 11 months ago
1992
Springer
This paper deals with the problem of inference under uncertain information. This is a generalization of a paper of Cardona et al. (1991a) where rules were not allowed to contain n...
UAI
1996
13 years 8 months ago
1996
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of th...
BMCBI
2010
13 years 7 months ago
2010
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
AIIA
2003
Springer
14 years 19 days 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...
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,...