An Axiomatization of Loglinear Models with an Application to the Model-Search Problem

Francesco M. Malvestuto
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:354-365, 1995.

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 an interaction model to be accepted or rejected without resorting to the numeric test. In principle, the best inferential mechanism should based on a sound and complete axiomatization of interaction models. We present a sound (and, probably incomplete) axiomatization which can be translated into a graphical inference procedure working with directed acyclic graphs.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-malvestuto95a, title = {An Axiomatization of Loglinear Models with an Application to the Model-Search Problem}, author = {Malvestuto, Francesco M.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {354--365}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/malvestuto95a/malvestuto95a.pdf}, url = {https://proceedings.mlr.press/r0/malvestuto95a.html}, 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 an interaction model to be accepted or rejected without resorting to the numeric test. In principle, the best inferential mechanism should based on a sound and complete axiomatization of interaction models. We present a sound (and, probably incomplete) axiomatization which can be translated into a graphical inference procedure working with directed acyclic graphs.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T An Axiomatization of Loglinear Models with an Application to the Model-Search Problem %A Francesco M. Malvestuto %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-malvestuto95a %I PMLR %P 354--365 %U https://proceedings.mlr.press/r0/malvestuto95a.html %V R0 %X 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 an interaction model to be accepted or rejected without resorting to the numeric test. In principle, the best inferential mechanism should based on a sound and complete axiomatization of interaction models. We present a sound (and, probably incomplete) axiomatization which can be translated into a graphical inference procedure working with directed acyclic graphs. %Z Reissued by PMLR on 01 May 2022.
APA
Malvestuto, F.M.. (1995). An Axiomatization of Loglinear Models with an Application to the Model-Search Problem. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:354-365 Available from https://proceedings.mlr.press/r0/malvestuto95a.html. Reissued by PMLR on 01 May 2022.

Related Material