Gaussian mixture modeling of nodes in Bayesian network according to maximal parental cliques
This work addresses the need for more flexible probabilistic modeling in Bayesian networks, but it appears incremental as it adapts existing mixture modeling techniques to a specific domain.
The paper tackles the problem of modeling node distributions in Bayesian networks by replacing linear Gaussian models with Gaussian mixture models, and introduces a double iteration algorithm combining expectation maximization and gradient descent for optimization, achieving perfect performance in experiments on graphs generated from real datasets.
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.