Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models

Bayesian networks are a powerful tool for modelling multivariate random variables. However, when applied in practice, for example, for industrial projects, problems arise because the existing learning and inference algorithms are not adapted to real data. This article discusses two learning and infe...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-01, Vol.11 (2), p.343
Main Authors: Deeva, Irina, Bubnova, Anna, Kalyuzhnaya, Anna V.
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian networks
Classification
classification models
Deep learning
Gaussian mixture model
Inference
Machine learning
Methods
Modelling
Networks
Nodes
parameters learning
Probabilistic models
Random variables
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Summary:Bayesian networks are a powerful tool for modelling multivariate random variables. However, when applied in practice, for example, for industrial projects, problems arise because the existing learning and inference algorithms are not adapted to real data. This article discusses two learning and inference problems on mixed data in Bayesian networks—learning and inference at nodes of a Bayesian network that have non-Gaussian distributions and learning and inference for networks that require edges from continuous nodes to discrete ones. First, an approach based on the use of mixtures of Gaussian distributions is proposed to solve a problem when the joint normality assumption is not confirmed. Second, classification models are proposed to solve a problem with edges from continuous nodes to discrete nodes. Experiments have been run on both synthetic datasets and real-world data and have shown gains in modelling quality.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11020343