Journal Article10.1109/69.494161
A guide to the literature on learning probabilistic networks from data
TL;DR: The literature review presented discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks.
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Abstract: The literature review presented discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The article avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples.
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Citations
Bayesian Network Classifiers
TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
•Book
Bayesian networks and decision graphs
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A tutorial on learning with Bayesian networks
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- 01 Feb 1999
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A Bayesian computer vision system for modeling human interactions
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Optimal structure identification with greedy search
TL;DR: This paper proves the so-called "Meek Conjecture", which shows that if a DAG H is an independence map of another DAG G, then there exists a finite sequence of edge additions and covered edge reversals in G such that H remains anindependence map of G and after all modifications G =H.
References
The EM algorithm for graphical association models with missing data
TL;DR: It is shown how the computational scheme of Lauritzen and Spiegelhalter (1988) can be exploited to perform the E-step of the EM algorithm when applied to findingmaximum likelihood estimates or penalized maximum likelihood estimates in hierarchical log-linear models and recursive models for contingency tables with missing data.
875
Theory refinement on Bayesian networks
Wray Buntine
- 13 Jul 1991
TL;DR: In this paper, the problem of theory refinement under uncertainty is reviewed in the context of Bayesian statistics, a theory of belief revision, reduced to an incremental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and can be incrementally refined from data.
826
Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative
TL;DR: In this article, the authors define and investigate classes of statistical models for the analysis of associations between variables, some of which are qualitative and some quantitative, and characterize the subclass of decomposable models where the statistical theory is especially simple.
815
•Proceedings Article
A Theory of Inferred Causation.
Judea Pearl,Thomas Verma +1 more
- 01 Jan 1991
TL;DR: In this paper, the theory of inferred causation is discussed, where causal ordering is defined as the ordering at which subsets of variables can be solved independently of others; in other systems, it follows the way a disturbance is propagated from one variable to others.
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