Probability Propagation in Generalized Inference Forms
Studia Logica 102 (4):913-929 (2014)
Abstract
Probabilistic inference forms lead from point probabilities of the premises to interval probabilities of the conclusion. The probabilistic version of Modus Ponens, for example, licenses the inference from \({P(A) = \alpha}\) and \({P(B|A) = \beta}\) to \({P(B)\in [\alpha\beta, \alpha\beta + 1 - \alpha]}\) . We study generalized inference forms with three or more premises. The generalized Modus Ponens, for example, leads from \({P(A_{1}) = \alpha_{1}, \ldots, P(A_{n})= \alpha_{n}}\) and \({P(B|A_{1} \wedge \cdots \wedge A_{n}) = \beta}\) to an according interval for P(B). We present the probability intervals for the conclusions of the generalized versions of Cut, Cautious Monotonicity, Modus Tollens, Bayes’ Theorem, and some SYSTEM O rules. Recently, Gilio has shown that generalized inference forms “degrade”—more premises lead to less precise conclusions, i.e., to wider probability intervals of the conclusion. We also study Adam’s probability preservation properties in generalized inference forms. Special attention is devoted to zero probabilities of the conditioning events. These zero probabilities often lead to different intervals in the coherence and the Kolmogorov approachDOI
10.1007/s11225-013-9513-4
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Citations of this work
Naive Probability: Model‐Based Estimates of Unique Events.Sangeet S. Khemlani, Max Lotstein & Philip N. Johnson-Laird - 2015 - Cognitive Science 39 (6):1216-1258.
Imprecise Uncertain Reasoning: A Distributional Approach.Gernot D. Kleiter - 2018 - Frontiers in Psychology 9.
Logic and Probability: Reasoning in Uncertain Environments – Introduction to the Special Issue.Matthias Unterhuber & Gerhard Schurz - 2014 - Studia Logica 102 (4):663-671.
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