Semantics and inference for recursive probability models (2000)by A. Pfeffer and D. Koller
Abstract:
In recent years, there have been several proposals that extend the expressive power of Bayesian networks with that of relational models. These languages open the possibility for the specification of recursive probability models, where a variable might depend on a potentially infinite (but finitely describable) set of variables. These models are very natural in a variety of applications, e.g., in temporal, genetic, or language models. In this paper, we provide a structured representation language that allows us to specify such models, a clean modeltheoretic semantics for this language, and a probabilistic inference algorithm that exploits the structure of the language for efficient queryanswering.
Download Information
A. Pfeffer and D. Koller (2000). "Semantics and inference for recursive probability models." Proceedings of the 17th National Conference on Artificial Intelligence (AAAI) (pp. 538544).


Bibtex citation
@inproceedings{Pfeffer+Koller:AAAI00,
title = {Semantics and inference for recursive probability models},
author = {A. Pfeffer and D. Koller},
booktitle = {Proceedings of the 17th National Conference on Artificial Intelligence (AAAI)},
address = {Austin, Texas},
month = {August},
year = 2000,
pages = {538544},
}
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