Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Distributed revision of composite beliefs
Artificial Intelligence
A logical framework for default reasoning
Artificial Intelligence
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Representing Bayesian networks within probabilistic Horn abduction
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Machine Learning
What is the most likely diagnosis?
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
an entropy-driven system for construction of probabilistic expert systems from databases
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Construction of preferred causal hypotheses for reasoning with uncertain knowledge
Construction of preferred causal hypotheses for reasoning with uncertain knowledge
Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies
Uncertainty Reasoning for the Semantic Web I
Context Hypothesization Using Probabilistic Knowledge
Fundamenta Informaticae
Parallel Abductive Query Answering in Probabilistic Logic Programs
ACM Transactions on Computational Logic (TOCL)
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Different ways of representing probabilistic relationships among the attributes of a domain ar examined, and it is shown that the nature of domain relationships used in a representation affects the types of reasoning objectives that can be achieved. Two well-known formalisms for representing the probabilistic among attributes of a domain. These are the dependence tree formalism presented by C.K. Chow and C.N. Liu (1968) and the Bayesian networks methodology presented by J. Pearl (1986). An example is used to illustrate the nature of the relationships and the difference in the types of reasoning performed by these two representations. An abductive type of reasoning objective that requires use of the known qualitative relationships of the domain is demonstrated. A suitable way to represent such qualitative relationships along with the probabilistic knowledge is given, and how an explanation for a set of observed events may be constituted is discussed. An algorithm for learning the qualitative relationships from empirical data using an algorithm based on the minimization of conditional entropy is presented.