{"type":"ScholarlyArticle","title":"PRISM: A Language for Symbolic-Statistical Modeling","authors":[{"name":"Taisuke Sato","slug":"taisuke-sato","given":"Taisuke","family":"Sato","affiliation":""},{"name":"Yoshitaka Kameya","slug":"yoshitaka-kameya","given":"Yoshitaka","family":"Kameya","affiliation":""}],"abstract":"We present an overview of symbolic-statistical modeling language PRISM whose programs are not only a probabilistic extension of logic programs but also able to learn from examples with the help of the EM learning algorithm. As a knowledge representation language appropriate for probabilistic reasoning, it can describe various types of symbolic-statistical modeling formalism known but unrelated so far in a single framework. We show by examples, together with learning results, that most popular probabilistic modeling formalisms, the hidden Markov model and Bayesian networks, are described by PRISM programs.","doi":null,"url":"https://ijcai.org/proceedings/1997/192","pdf":"https://ijcai.org/proceedings/1997/0192.pdf","markdown":"https://ijcai.org/proceedings/1997/192.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/192","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Probabilistic Reasoning","track":"Probabilistic Reasoning","number":192,"pages":{"first":1330,"last":1338},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}