2017/04/30 by Giuseppe Sanfilippo, Sanfilippo, Giuseppe, Niki Pfeifer +3
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Mathematics #Logic, Reasoning, and Knowledge #Probability (math.PR) #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1705.00385
openalex publication_date 2017/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modus ponens (from A and "if A then C" infer C, short: MP) is one of the most basic inference rules. The probabilistic MP allows for managing uncertainty by transmitting assigned uncertainties from the premises to the conclusion (i.e., from P(A) and P(C|A) infer P(C)). In this paper, we generalize the probabilistic MP by replacing A by the conditional event A|H. The resulting inference rule involves iterated conditionals (formalized by conditional random quantities) and propagates previsions from the premises to the conclusion. Interestingly, the propagation rules for the lower and the upper bounds on the conclusion of the generalized probabilistic MP coincide with the respective bounds on the conclusion for the (non-nested) probabilistic MP.