A guide to the literature on learning probabilistic networks from data

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Abstract

This literature review 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 presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples.

Original languageEnglish
Pages (from-to)195-210
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume8
Issue number2
DOIs
Publication statusPublished - 1 Dec 1996

Keywords

  • Bayesian networks
  • Graphical models
  • Hidden variables
  • Knowledge discovery
  • Learning
  • Learning structure
  • Probabilistic networks

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