PRISM: A Language for Symbolic-Statistical Modeling
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Probabilistic Reasoning. 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.
@inproceedings{ijcai1997p192,
title = {PRISM: A Language for Symbolic-Statistical Modeling},
author = {Sato, Taisuke and Kameya, Yoshitaka},
booktitle = {Proceedings of the Fifteenth International Joint Conference on
Artificial Intelligence, {IJCAI-97}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Martha E. Pollack},
pages = {1330--1338},
year = {1997},
note = {Probabilistic Reasoning},
url = {https://ijcai.org/proceedings/1997/192},
}
TY - CPAPER TI - PRISM: A Language for Symbolic-Statistical Modeling AU - Sato, Taisuke AU - Kameya, Yoshitaka T2 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97 PB - International Joint Conferences on Artificial Intelligence PY - 1997 SP - 1330 EP - 1338 UR - https://ijcai.org/proceedings/1997/192 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
