Handling epistemic and aleatory uncertainties in probabilistic circuits
dc.contributor.author | Cerutti, F. | |
dc.contributor.author | Kaplan, L. M. | |
dc.contributor.author | Kimmig, A. | |
dc.contributor.author | Şensoy, Murat | |
dc.date.accessioned | 2023-07-14T11:52:27Z | |
dc.date.available | 2023-07-14T11:52:27Z | |
dc.date.issued | 2022-04 | |
dc.identifier.issn | 0885-6125 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/8511 | |
dc.identifier.uri | https://link.springer.com/article/10.1007/s10994-021-06086-4 | |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence | |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Machine Learning | |
dc.rights | restrictedAccess | |
dc.title | Handling epistemic and aleatory uncertainties in probabilistic circuits | en_US |
dc.type | Article | en_US |
dc.peerreviewed | yes | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0001-8806-4508 & YÖK ID 41438) Şensoy, Murat | |
dc.contributor.ozuauthor | Şensoy, Murat | |
dc.identifier.volume | 111 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.startpage | 1259 | en_US |
dc.identifier.endpage | 1301 | en_US |
dc.identifier.wos | WOS:000740624700001 | |
dc.identifier.doi | 10.1007/s10994-021-06086-4 | en_US |
dc.subject.keywords | Bayesian learning | en_US |
dc.subject.keywords | Imprecise probabilities | en_US |
dc.subject.keywords | Probabilistic circuit | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85122670881 | |
dc.relation.publicationcategory | Article - International Refereed Journal - Institutional Academic Staff |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
Share this page