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Uncertainty in Artificial Intelligence
d-Separation: From Theorems to Algorithms
edited_book
Author(s):
Dan Geiger
,
Thomas Verma
,
Judea Pearl
Publication date
(Print):
1990
Publisher:
Elsevier
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International Journal of Management Studies
Author and book information
Book Chapter
Publication date (Print):
1990
Pages
: 139-148
DOI:
10.1016/B978-0-444-88738-2.50018-X
SO-VID:
a74bd17f-34e3-4324-ad91-6863a5c0871a
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https://www.elsevier.com/tdm/userlicense/1.0/
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Book chapters
pp. 29
Constructing the Pignistic Probability Function in a Context of Uncertainty
pp. 69
Causal Networks: Semantics and Expressiveness* *This work was partially supported by the National Science Foundation Grant #IRI-8610155. “Graphoids: A Computer Representation for Dependencies and Relevance in Automated Reasoning (Computer Information Science).”
pp. 129
An Introduction to Algorithms for Inference in Belief Nets
pp. 139
d-Separation: From Theorems to Algorithms
pp. 149
Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling
pp. 163
A Tractable Inference Algorithm for Diagnosing Multiple Diseases1 1This work was supported by the NSF under Grant IRI-8703710, and by the NLM under Grant R01-LM04529.
pp. 169
Axioms for Probability and Belief-Function Propagation
pp. 173
Evidence Absorption and Propagation Through Evidence Reversals
pp. 209
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
pp. 217
Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-Off Precision and Complexity* *This work was partially supported by the Defense Advanced Research Projects Agency (DARPA) contract F30602-85-C0033. Views and conclusions contained in this paper are those of the authors and should not be interpreted as representing the official opinion or policy of DARPA or the U.S. Government.
pp. 221
Simulation Approaches to General Probabilistic Inference on Belief Networks
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