Learning Bayesian Networks

Title : Learning Bayesian Networks

Pub Date : -

Author : Richard E. Neapolitan

Publisher: Northeastern Illinois University

ISBN : -

Overview :

Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. During the 1980’s, a good deal of related research was done on developing Bayesian networks (belief networks, causal networks, influence diagrams), algorithms for performing inference with them, and applications that used them. However, the work was scattered throughout research articles. My purpose in writing the 1990 text Probabilistic Reasoning in Expert Systems was to unify this research and establish a textbook and reference for the field which has come to be known as ‘Bayesian networks.’ The 1990’s saw the emergence of excellent algorithms for learning Bayesian networks from data. However,by 2000 there still seemed to be no accessible source for ‘learning Bayesian networks.’ Similar to my purpose a decade ago, the goal of this text is to provide such a source.

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