
AbstractThis paper investigates probabilistic logics endowed with independence relations. We review propositional probabilistic languages without and with independence. We then consider graph-theoretic representations for propositional probabilistic logic with independence; complexity is analyzed, algorithms are derived, and examples are discussed. Finally, we examine a restricted first-order probabilistic logic that generalizes relational Bayesian networks.
Artificial Intelligence, Applied Mathematics, Linear and multilinear programming, Sets of probability distributions, Graph-theoretic models, Software, 004, Probabilistic logic, Theoretical Computer Science
Artificial Intelligence, Applied Mathematics, Linear and multilinear programming, Sets of probability distributions, Graph-theoretic models, Software, 004, Probabilistic logic, Theoretical Computer Science
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