Handling epistemic and aleatory uncertainties in probabilistic circuits

Name: Handling epistemic and aleatory uncertainties in probabilistic circuits
Author: Cerutti, F., Kaplan, L. M., Kimmig, A., Şensoy, Murat

İsim	Handling epistemic and aleatory uncertainties in probabilistic circuits
Yazar	Cerutti, F., Kaplan, L. M., Kimmig, A., Şensoy, Murat
Basım Tarihi:	2022-04
Basım Yeri	- Springer
Konu	Bayesian learning, Imprecise probabilities, Probabilistic circuit
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0885-6125
Kayıt Numarası	118af8df-0649-4c2d-8e4b-e0a904b71a82
Lokasyon	Computer Science
Tarih	2022-04
Notlar	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Handling epistemic and aleatory uncertainties in probabilistic circuits

Yazar Cerutti, F., Kaplan, L. M., Kimmig, A., Şensoy, Murat

Basım Tarihi 2022-04

Basım Yeri - Springer

Konu Bayesian learning, Imprecise probabilities, Probabilistic circuit

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0885-6125

Kayıt Numarası 118af8df-0649-4c2d-8e4b-e0a904b71a82

Lokasyon Computer Science

Tarih 2022-04

Notlar United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence

Örnek Metin 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