Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Probabilistic similarity networks
Probabilistic similarity networks
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
A linear constraint satisfaction approach to cost-based abduction
Artificial Intelligence
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Independence Semantics for BKBs
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Generalizing Knowledge Representation Rules for Acquiring and Validating Uncertain Knowledge
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On automatic knowledge validation for Bayesian knowledge bases
Data & Knowledge Engineering
A framework for reasoning under uncertainty with temporal constraints
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
On a framework for the prediction and explanation of changing opinions
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Fusing multiple Bayesian knowledge sources
International Journal of Approximate Reasoning
Hidden Source Behavior Change Tracking and Detection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extends the well-known Bayes Networks (BN), by naturally allowing for context-specific independence and for cycles in the directed graph. We present a semantics for BKBs that facilitate handling of marginal probabilities, as well as finding most probable explanations.Complexity of reasoning with BKBs is NP hard, as for Bayes networks, but in addition, deciding consistency is also NP-hard. In special cases that problem does not occur. Computation of marginal probabilities in BKBs is another hard problem, hence approximation algorithms are necessary – stochastic sampling being a commonly used scheme. Good performance requires importance sampling, a method that works for BKBs with cycles is developed.