Semiparametric Bayesian networks

Atienza González, David, Bielza Lozoya, María Concepción ORCID: and Larrañaga Múgica, Pedro María ORCID: (2021). Semiparametric Bayesian networks. "Information Sciences", v. 584 ; pp. 564-582. ISSN 0020-0255.


Title: Semiparametric Bayesian networks
Item Type: Article
Título de Revista/Publicación: Information Sciences
Date: November 2021
ISSN: 0020-0255
Volume: 584
Freetext Keywords: Bayesian networks, Kernel density estimation, Semiparametric model, Continuous data
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones. We demonstrate that semiparametric Bayesian networks generalize two well-known types of Bayesian networks: Gaussian Bayesian networks and kernel density estimation Bayesian networks. For this purpose, we consider two different conditional probability distributions required in a semiparametric Bayesian network. In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data. To realize this, we employ as core function based on cross-validation. In addition, using a validation dataset, we apply an early-stopping criterion to avoid overfitting. To evaluate the applicability of the proposed algorithm, we conduct an exhaustive experiment on synthetic data sampled by mixing linear and nonlinear functions, multivariate normal data sampled from Gaussian Bayesian networks, real data from the UCI repository, and bearings degradation data. Asa result of this experiment, we conclude that the proposed algorithm accurately learns the combination of parametric and nonparametric components, while achieving a performance comparable with those provided by state-of-the-art methods._ 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CCBY-NC-ND license (

Funding Projects

Government of Spain
Government of Spain

More information

Item ID: 72639
DC Identifier:
OAI Identifier:
DOI: 10.1016/j.ins.2021.10.074
Official URL:
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 15 Feb 2023 07:19
Last Modified: 15 Feb 2023 11:05
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