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
•Posted Content
Theory Refinement on Bayesian Networks
TL;DR: Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation", etc, and are an incremental variant of batch learning algorithms from the literature so can work well in batch and incremental mode.
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Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information
TL;DR: An algorithm for automated construction of a sparse Bayesian network given an unstructured probabilistic model and causal domain information from an expert has been developed and implemented to obtain a network that explicitly reveals as much information regarding conditional independence as possible.
Decision theoretic subsampling for induction on large databases
Ron Musick,Jason Catlett,Stuart Russell +2 more
- 27 Jun 1993
TL;DR: This paper addresses the questions of assessing when the choice may be made with a given expected error, and determining a sampling strategy that minimizes the computation cost of making it, by using a subsample for this calculation.
BIFROST—block recursive models induced from relevant knowledge, observations, and statistical techniques
Søren Højsgaard,Bo Thiesson +1 more
TL;DR: The theoretical background for a program for establishing expert systems on the basis of observations and expert knowledge and various model selection methods for automatic model selection are presented.