Author
Cerutti, F., Kaplan, L. M., Kimmig, A., Şensoy, Murat
Publication Date
2022-04
Publication Place
-
Springer
Subject
Bayesian learning, Imprecise probabilities, Probabilistic circuit
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0885-6125
Record ID
118af8df-0649-4c2d-8e4b-e0a904b71a82
Library Location
Computer Science
Date
2022-04
Notes
United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence
Sample Text
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to Bayesian inference of posterior distributions that overcomes the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning that includes Bayesian networks as well as several instances of probabilistic logic. We provide an algorithm for Bayesian inference of posterior distributions from sparse, albeit complete, observations, and for deriving inferences and their confidences keeping track of the dependencies between variables when they are manipulated within the unifying computational formalism provided by probabilistic circuits. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities. We achieve better estimation of epistemic uncertainty than state-of-the-art approaches, including highly engineered ones, while being able to handle general circuits and with just a modest increase in the computational effort compared to using point probabilities.
DOI
10.1007/s10994-021-06086-4
Cilt
111
