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arXiv Machine Learning By Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz, Ruben Arma\~nanzas

Semi-Linear Gaussian Bayesian Networks: Enhancing Flexibility in Node Dependencies via penalized B-Splines

Read the original on arXiv Machine Learning →

The paper introduces Semi-Linear Gaussian Bayesian Networks (SLGBN), a new class of continuous Bayesian networks that combine linear Gaussian models with nonlinear conditional mean functions estimated via penalized B-splines within generalized additive models. SLGBN preserves Gaussian conditional distributions while allowing nonlinear relationships, and the authors provide parameter estimation procedures and a greedy hill‑climbing algorithm for structure learning. Experiments comparing SLGBN to semiparametric and linear Gaussian models show that SLGBN achieves better goodness‑of‑fit and structure recovery, especially when using the EBIC* score, while maintaining computational efficiency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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