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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References
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Selecting Models from Data: AI and Statistics IV
P. Cheeseman,R. W. Oldford +1 more
- 01 May 1994
The Quantification of Judgment: Some Methodological Suggestions
TL;DR: In this article, an ideal Assessor is hypothesized and his behavior is investigated under a number of such methods, including those suggested by de Finetti and others, and the implications of these methods for the theory of personal probability are discussed.
Operations for learning with graphical models
TL;DR: In this article, a multidisciplinary review of empirical, statistical learning from a graphical model perspective is presented, including decomposition, differentiation, and manipulation of probability models from the exponential family.