Generative Modeling with Failure in PRISM
Abstract
PRISM is a logic-based Turing-complete symbolic-statistical modeling language with a built-in parameter learning routine. In this paper,we enhance the modeling power of PRISM by allowing general PRISM programs to fail in the generation process of observable events. Introducing failure extends the class of definable distributions but needs a generalization of the semantics of PRISM programs. We propose a three valued probabilistic semantics and show how failure enables us to pursue constraint-based modeling of complex statistical phenomena.
Cite
Text
Sato et al. "Generative Modeling with Failure in PRISM." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Sato et al. "Generative Modeling with Failure in PRISM." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/sato2005ijcai-generative/)BibTeX
@inproceedings{sato2005ijcai-generative,
title = {{Generative Modeling with Failure in PRISM}},
author = {Sato, Taisuke and Kameya, Yoshitaka and Zhou, Neng-Fa},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {847-852},
url = {https://mlanthology.org/ijcai/2005/sato2005ijcai-generative/}
}