Classical iterative proportional scaling of log-linear models with rational maximum likelihood estimator

In this work we investigate multipartition models, the subset of log-linear models for which one can perform the classical iterative proportional scaling (IPS) algorithm to numerically compute the maximum likelihood estimate (MLE). Multipartition models include families of models such as hierarchical models and balanced, stratified staged trees. We define a sufficient condition, called the Generalized Running Intersection Property (GRIP), on the matrix representation of a multipartition model under which the classical IPS algorithm produces the exact MLE in one cycle. In this case, the MLE is a rational function of the data. Additionally we connect the GRIP to the toric fiber product and to previous results for hierarchical models and balanced, stratified staged trees. This leads to a characterization of balanced, stratified staged trees in terms of the GRIP.

Subjects

Hierarchical models

Iterative proportional scaling

Log-linear models

Maximum likelihood estimation

Staged tree models

Toric fiber product

DDC Class

004: Computer Sciences

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publishedVersion

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https://creativecommons.org/licenses/by/4.0/

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1-s2.0-S0888613X23001743-main.pdf

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Main Article

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903.07 KB

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Classical iterative proportional scaling of log-linear models with rational maximum likelihood estimator