T. Sato and Y. Kameya. PRISM: A symbolic-statistical modeling language. In Proceedings of the 15th International Joint Conference on Artificial Intel l igence (IJCAI-1997. Home/Search Document Not in Database Summary Related Articles Check |
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Approximate Inference for First-Order Probabilistic Languages - Russell (2001) (8 citations) (Correct)
....symbols of the knowledge base [Halpern, 1990] Although wildly undecidable in full generality, highly restricted FOPLs appear to be practical, especially with finite models. Two threads have arisen, based on semantic networks (e.g. Koller and Pfeffer, 1998] and logic programming (e.g. [Sato and Kameya, 1997]) In this paper, we focus on the family of relational probability models (RPMs) Pfeffer, 2000] although our ideas apply equally to other languages. RPMs, like semantic networks, are based on classes containing instances, with each instance possessing attributes. See Section 2 for details. ....
T. Sato and Y. Kameya. PRISM: A symbolic-statistical modeling language. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pages 1330--1335, Nagoya, Japan, August 1997. Morgan Kaufmann.
PRISM User's Manual - Prism Programming Language Self-citation (Kameya) (Correct)
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Sate, T. and Kameya, Y., PRISM: A symbolic-statistical modeling language, In Proc. of 15th International Joint Conference on Artificial Intelligence (IJCAI97), pp.1330-1335, 1997.
A Graphical Method for Parameter Learning of.. - Kameya, Ueda, Sato (1999) (1 citation) Self-citation (Sato Kameya) (Correct)
....for HMMs. We show that, given appropriate data structure, Baum Welch algorithm can be simulated by our graph based EM algorithm. 1 Introduction To capture uncertain phenomena in a symbolic framework, we have been devel oping a symbolic statistical modeling language PRISM in the past years [10, 11]. PRISM programs is a probabilistic extension of logic programs based on distributional semantics, and its programming system has a built in mechanism for statistical parameter learning from observed data. For parameter learning, we adopt the EM algorithm, an iterative method for maximum ....
....system has a built in mechanism for statistical parameter learning from observed data. For parameter learning, we adopt the EM algorithm, an iterative method for maximum likelihood estima tion (MLE) With this learning ability built into the expressive power of firstorder logic, as shown in [10], PRISM not only covers existing symbolic statistical models ranging from hidden Markov models (HMMs) 1, 8] to Bayesian networks (BNs) 6] and to probabilistic context free grammars (PCFGs) 1] but can smoothly model the complicated interaction between gene inheritance and a tribal social system ....
Sato, T., and Kameya, Y., PRISM: a symbolic-statistical modeling language, Proc. of the 15th Intl. Joint Conf. on Artificial Intelligence, pp.1330-1335, 1997.
Logical Bayesian Networks and Their Relation to.. - Fierens.. (2005) (Correct)
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T. Sato and Y. Kameya. PRISM: A symbolic-statistical modeling language. In Proceedings of the 15th International Joint Conference on Artificial Intel l igence (IJCAI-1997.
Probabilistic Logic Learning - De Raedt, Kersting (2004) (2 citations) (Correct)
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T. Sato and Y. Kameya. PRISM: A Symbolic-- Statistical Modeling Language. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pages 1330--1339, Nagoya, Japan, 1997. Morgan Kaufmann.
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