Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Expert Systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Evidence Absorption and Propagation through Evidence Reversals
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Discovery of Bayesian Neworks From Data with Maintenance of Partially Oriented Graphs
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Bayesian Network Mining System
Proceedings of the International Symposium on "Intelligent Information Systems X"
Well-Structured Program Graphs and the Issue of Local Computations
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
A hybrid algorithm to compute marginal and joint beliefs in Bayesian networks and its complexity
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Very large Bayesian multinets for text classification
Future Generation Computer Systems
Very large Bayesian multinets for text classification
Future Generation Computer Systems
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Bayesian networks have many practical applications due to their capability to represent joint probability distribution over many variables in a compact way. Though there exist many algorithms for learning Bayesian networks from data, they are not satisfactory because the learned networks usually are not suitable directly for reasoning as they need to be transformed to some other form (tree, polytree, hypertree) statically or dynamically, and this transformation is not trivial [25]. So far only a restricted class of very simple Bayesian networks: trees and poly-trees are directly applicable in reasoning. This paper defines and explores a new class of networks: the Structured Bayesian Networks. Two methods of reasoning are outlined for this type of networks. Possible methods of learning from data are indicated. Similarity to hierarchical networks is pointed at.