---
title: "PRISM: A Language for Symbolic-Statistical Modeling"
authors:
  - "Taisuke Sato"
  - "Yoshitaka Kameya"
doi: ""
url: "https://ijcai.org/proceedings/1997/192"
pdf: "https://ijcai.org/proceedings/1997/0192.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "1330-1338"
track: "Probabilistic Reasoning"
keywords:
---

# PRISM: A Language for Symbolic-Statistical Modeling

Taisuke Sato, Yoshitaka Kameya

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 1330-1338.

## 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.