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
Using causal information and local measures to learn Bayesian networks
Wai Lam,Fahiem Bacchus +1 more
- 09 Jul 1993
TL;DR: In this paper, the authors present a new local way of computing the description length and modify their algorithm so that it can take into account partial domain information that might be provided by a domain expert.
68
•Proceedings Article
Chain graphs for learning
Wray Buntine
- 18 Aug 1995
TL;DR: In this article, a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and Markov (undirected) networks is given, which combines properties of the two.
67
A characterization of the dirichlet distribution with application to learning Bayesian networks
Dan Geiger,David Heckerman +1 more
- 18 Aug 1995
TL;DR: This work provides a new characterization of the Dirichlet distribution that implies that under assumptions made by several previous authors for learning belief networks, aDirichlet prior on the parameters is inevitable.
65
•Posted Content
An Entropy-based Learning Algorithm of Bayesian Conditional Trees
TL;DR: In this paper, a modification of Chow and Liu's learning algorithm in the context of handwritten digit recognition is presented, which directs the user to group digits into several classes consisting of digits that are hard to distinguish and then construct an optimal conditional tree representation for each class of digits instead of for each single digit.
64
An entropy-based learning algorithm of Bayesian conditional trees
Dan Geiger
- 01 Jun 1992
TL;DR: The modified algorithm directs the user to group digits into several classes consisting of digits that are hard to distinguish and then constructing an optimal conditional tree representation for each class of digits instead of for each single digit as done by Chow and Liu (1968).
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