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Log‐Linear Models in Expert Systems
Author(s) -
Castillo E.,
Mora E.,
Alvarez E.
Publication year - 1994
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.1994.tb00342.x
Subject(s) - multinomial distribution , computer science , simple (philosophy) , parametric statistics , expert system , sampling (signal processing) , linear model , data mining , machine learning , artificial intelligence , mathematics , statistics , philosophy , epistemology , filter (signal processing) , computer vision
The paper describes how log‐linear models can be used to deal with uncertainty in expert systems, avoiding the common problems of many probability‐based expert systems. After a general introduction to log‐linear models, including hierarchical models, maximum likelihood estimation for poissonian and multinomial sampling is described and parametric and structural learning methods are illustrated by simple examples. Finally, a traffic engineering example is given.