Models and Algorithms for Probabilistic and Bayesian Logic

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 Bayesian networks are also studied.

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