نویسنده
Cerutti, F., Kaplan, L. M., Kimmig, A., Şensoy, Murat
تاریخ انتشار
2022-04
محل انتشار
-
Springer
موضوع
Bayesian learning, Imprecise probabilities, Probabilistic circuit
نوع
دوره ای
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
0885-6125
شماره ثبت
118af8df-0649-4c2d-8e4b-e0a904b71a82
محل کتابخانه
Computer Science
تاریخ
2022-04
یادداشتها
United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence
متن نمونه
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
