A guide to the literature on learning probabilistic networks from
data
Knowledge and Data Engineering, IEEE Transactions on, 8 (2):
195-210 (April 1996)DOI: 10.1109/69.494161
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
