Artificial Intelligence
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Reasoning about Uncertainty
The emergence of reasons conjecture
Journal of Applied Logic - Special issue on combining probability and logic
Bayesian Nets And Causality: Philosophical And Computational Foundations
Bayesian Nets And Causality: Philosophical And Computational Foundations
A note on the least informative model of a theory
CiE'10 Proceedings of the Programs, proofs, process and 6th international conference on Computability in Europe
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This paper develops connections between objective Bayesian epistemology-which holds that the strengths of an agent's beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal-and probabilistic logic. After introducing objective Bayesian epistemology over propositional languages, the formalism is extended to handle predicate languages. A rather general probabilistic logic is formulated and then given a natural semantics in terms of objective Bayesian epistemology. The machinery of objective Bayesian nets and objective credal nets is introduced and this machinery is applied to provide a calculus for probabilistic logic that meshes with the objective Bayesian semantics.