Adaptive Learning Algorithm for Bayesian Networks Based on Kernel Mixtures Distributions
Bayesian networks (BN) are a powerful tool for modelling multivariate random variables. However, when applied, for example, for industrial projects, problems arise because it does not adapt the existing learning and inference algorithms to real data, since real data are usually
represented as multivariate random variables with non-Gaussian distribution. This article discusses structural and parametric learning problems in Bayesian networks from data which has non-Gaussian distribution and non-linear relations. We propose an algorithm based on the use of mixtures of Gaussian distributions to solve a problem when the joint normality assumption is not confirmed. A feature of the algorithm is that the mixture distributions are adopted for the data right at the stage of structural learning and are used further in the process of parameters learning. Experiments have been run on both synthetic datasets and real-world data and have shown gains in modelling quality.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.