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JMLR
2010
140views more  JMLR 2010»
14 years 6 months ago
Learning Non-Stationary Dynamic Bayesian Networks
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Joshua W. Robinson, Alexander J. Hartemink
IJPRAI
1998
100views more  IJPRAI 1998»
14 years 11 months ago
Obtaining The Correspondence between Bayesian and Neural Networks
We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we c...
Athena Stassopoulou, Maria Petrou
ISMIS
2003
Springer
15 years 4 months ago
Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks
Abstract. Multiply sectioned Bayesian networks (MSBNs) were originally proposed as a modular representation of uncertain knowledge by sectioning a large Bayesian network (BN) into ...
Cory J. Butz, H. Geng
ACIVS
2006
Springer
15 years 3 months ago
Interactive Learning of Scene Context Extractor Using Combination of Bayesian Network and Logic Network
The vision-based scene understanding technique that infers scene-interpreting contexts from real-world vision data has to not only deal with various uncertain environments but also...
Keum-Sung Hwang, Sung-Bae Cho
BMCBI
2007
164views more  BMCBI 2007»
14 years 11 months ago
Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
Background: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a...
Peng Li, Chaoyang Zhang, Edward J. Perkins, Ping G...