2023-07-02-jahn23a.md

abstract	openreview	title	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Probabilistic "if A then B" rules are typically formalized as Bayesian conditionals P(B|A), as many (e.g., Pearl) have argued that Bayesian conditionals are the correct way to think about such rules. However, there are challenges with standard inferences such as modus ponens and modus tollens that might make probabilistic material implication a better candidate at times for rule-based systems employing forward-chaining; and arguably material implication is still suitable when information about prior or conditional probabilities is not available at all. We investigate a generalization of probabilistic material implication and Bayesian conditionals that combines the advantages of both formalisms in a systematic way and prove basic properties of the generalized rule, in particular, for inference chains in graphs.	tMgeREAdK0F	Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	jahn23a	0	Investigating a Generalization of Probabilistic Material Implication and {B}ayesian Conditionals	932	940	932-940	932	false	Jahn, Michael and Scheutz, Matthias	given family Michael Jahn given family Matthias Scheutz	2023-07-02		Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence	216	inproceedings	date-parts 2023 7 2	https://proceedings.mlr.press/v216/jahn23a/jahn23a.pdf