2005年
Generative Modeling with Failure in PRISM
19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05)
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- 開始ページ
- 847
- 終了ページ
- 852
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- 出版者・発行元
- IJCAI-INT JOINT CONF ARTIF INTELL
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.
- リンク情報
- ID情報
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- Web of Science ID : WOS:000290233000136