Gaussian mixture model on nodes of Bayesian network given maximal parental cliques

• URL: http://arxiv.org/abs/2204.09532v1
• Date: Wed, 20 Apr 2022 15:14:01 GMT
• Title: Gaussian mixture model on nodes of Bayesian network given maximal parental cliques
• Authors: Yiran Dong Chuanhou Gao
• Abstract summary: We will explain why and how we use Gaussian mixture models in network. We propose a new method, called double iteration algorithm, to optimize the mixture model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper uses Gaussian mixture model instead of linear Gaussian model to fit the distribution of every node in Bayesian network. We will explain why and how we use Gaussian mixture models in Bayesian network. Meanwhile we propose a new method, called double iteration algorithm, to optimize the mixture model, the double iteration algorithm combines the expectation maximization algorithm and gradient descent algorithm, and it performs perfectly on the Bayesian network with mixture models. In experiments we test the Gaussian mixture model and the optimization algorithm on different graphs which is generated by different structure learning algorithm on real data sets, and give the details of every experiment.

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