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J. Pearl, Thomas Verma (1987)
The Logic of Representing Dependencies by Directed Graphs
R. Dechter (1996)
Bucket elimination: A unifying framework for probabilistic inference
S. Lauritzen, D. Spiegelhalter (1990)
Local computations with probabilities on graphical structures and their application to expert systemsJournal of the royal statistical society series b-methodological, 50
P. Spirtes (1994)
Conditional Independence in Directed Cyclic Graphical Models for Feedback
F. Jensen, S. Lauritzen, K. Olesen (1990)
Bayesian updating in causal probabilistic networks by local computations, 4
G Shafer (1996)
Probabilistic expert system. CBMS-NSF Regional Conference Series in Applied Mathematics, vol 67
R. Neapolitan (2012)
Probabilistic reasoning in expert systems - theory and algorithms
D. Geiger, Thomas Verma, J. Pearl (2013)
d-Separation: From Theorems to Algorithms
R. Cowell, A. Dawid, S. Lauritzen, D. Spiegelhalter (1999)
Probabilistic Networks and Expert Systems
(1988)
Influence diagrams and D-separation
G. Shafer (1996)
Probabilistic expert systems, 67
P. Hájek, T. Havránek, R. Jirousek (1992)
Uncertain information processing in expert systems
R. Neapolitan (1990)
Probabilistic reasoning in expert systems
(1989)
Axioms and algorithms for inferences involving conditional independence
P Spirtes, C Glymour, R Scheines (1993)
Causation, prediction, and search. Lecture notes in statistics 81
J. Pearl (1991)
Probabilistic reasoning in intelligent systems - networks of plausible inference
Michael Jordan (1999)
Learning in Graphical Models, 89
P. Spirtes, C. Glymour, R. Scheines (1993)
Causation, prediction, and search
R Dechter (1996)
Proceedings of the twelthth conference on uncertainty in artificial intelligence
FV Jensen (1996)
An introduction to Bayesian networks
P Hájek, T Havránek, R Jirouśek (1992)
Information processing in expert systems
D. Heckerman (1999)
A Tutorial on Learning with Bayesian Networks
Consider a family $${(X_i)_{i \in I}}$$ of random variables endowed with the structure of a Bayesian network, and a subset S of I. This paper examines the problem of computing the probability distribution of the subfamily $${(X_{a})_{a \in S}}$$ (respectively the probability distribution of $${ (X_{b})_{b \in {\bar{S}}}}$$ , where $${{\bar{S}} = I - S}$$ , conditional on $${(X_{a})_{a \in S}}$$ ). This paper presents some theoretical results that makes it possible to compute joint and conditional probabilities over a subset of variables by computing over separate components. In other words, it is demonstrated that it is possible to decompose this task into several parallel computations, each related to a subset of S (respectively of $${{\bar{S}}}$$ ); these partial results are then put together as a final product. In computing the probability distribution over $${(X_a)_{a \in S}}$$ , this procedure results in the production of a structure of level two Bayesian network structure for S.
Artificial Intelligence Review – Springer Journals
Published: Oct 22, 2009
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