Abstract
A good strategy to save computational time in a model-search problem consists in endowing the search procedure with a mechanism of logical inference, which sometimes allows a loglinear model to be accepted or rejected on logical grounds, without resorting to the numeric test. In principle, the best inferential mechanism should based on a complete axiomatization of loglinear models. We present a (probably incomplete) axiomatization, which can be translated into a graphical inference procedure working with directed acyclic graphs, and show how it can be applied to find an efficient solution to the model-search problem.
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© 1996 Springer-Verlag New York, Inc.
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Malvestuto, F.M. (1996). An Axiomatization of Loglinear Models with an Application to the Model-Search Problem. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_17
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DOI: https://doi.org/10.1007/978-1-4612-2404-4_17
Publisher Name: Springer, New York, NY
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