Local expression languages for probabilistic dependence: a preliminary report

Authors:
Bruce D'Ambrosio
Affiliations:
Department of Computer Science, Oregon State University
Venue:
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Year:
1991

Citing 6
Cited 0

A probabilistic causal model for diagnostic problem solving, part one: integrating symbolic causal inference with numeric probabilistic inference

IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
Evaluating influence diagrams

Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Incremental construction and evaluation of defeasible probabilistic models

International Journal of Approximate Reasoning
A Tractable Inference Algorithm for Diagnosing Multiple Diseases

UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Symbolic probabilistic inference in belief networks

AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a generalization of the local expression language used in the Symbolic Probabilistic Inference (SPI) approach to inference in belief nets [1], [8]. The local expression language in SPI is the language in which the dependence of a node on its antecedents is described. The original language represented the dependence as a single monolithic conditional probability distribution. The extended language provides a set of operators (*, +, and -) which can be used to specify methods for combining partial conditional distributions. As one instance of the utility of this extension, we show how this extended language can be used to capture the semantics, representational advantages, and inferential complexity advantages of the "noisy or" relationship.