Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing Bayesian networks within probabilistic Horn abduction
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action
Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action
A Language for Construction of Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic construction of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Computer Methods and Programs in Biomedicine
Enhancing the automatic generation of hints with expert seeding
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
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We present a method for dynamically generating Bayesian networks from knowledge bases consisting of first-order probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network. We define the concept of d-separation for knowledge bases and prove that a knowledge base with independence conditions defined by d-separation is a complete specification of a probability distribution. We present a network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P(Q|E). We prove the algorithm to be correct.