PRISM: a language for symbolic-statistical modeling
Taisuke Sato, Yoshitaka Kameya · 1997
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. 1