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
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Optimal structure identification with greedy search
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Ron Kohavi,George H. John,R. Long,D. Manley,Karl Pfleger +4 more
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TL;DR: The problems M LC++ aims to solve, the design of MLC++, and the current functionality are discussed, including the attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible.
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An algorithm for deciding if a set of observed independencies has a causal explanation
Thomas Verma,Judea Pearl +1 more
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TL;DR: In this article, the authors address the question of deciding whether there exists a causal model that explains ALL the observed dependencies and independencies, given a list M of conditional independence statements, and present an effective algorithm that tests for the existence of such a dag, and produces one, if it exists.