Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures

Tomi Silander; Janne Leppä-aho; Elias Jääsaari; Teemu Roos

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arXiv · 2408.14935

Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures

Tomi Silander·Janne Leppä-aho·Elias Jääsaari·Teemu Roos

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Abstract

We introduce an information theoretic criterion for Bayesian network structure learning which we call quotient normalized maximum likelihood (qNML). In contrast to the closely related factorized normalized maximum likelihood criterion, qNML satisfies the property of score equivalence. It is also decomposable and completely free of adjustable hyperparameters. For practical computations, we identify a remarkably accurate approximation proposed earlier by Szpankowski and Weinberger. Experiments on both simulated and real data demonstrate that the new criterion leads to parsimonious models with good predictive accuracy.

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Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos. 2024-08-27. Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. https://arxiv.org/abs/2408.14935

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