Abstract
Abstract: 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.
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