Group for Research in Decision Analysis

G-95-02

Models and Algorithms for Probabilistic and Bayesian Logic

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An overview is given, with new results, of mathematical models and algorithms for probabilistic logic, probabilistic entailment and various extensions. Analytical and numerical solutions are considered, the former leading to automated generation of theorems in the theory of probabilities. Ways to restore consistency and relationship with belief networks (Andersen and Hooker's Bayesian logic) are also studied.

, 18 pages