Abstract: This article presents a new Gaussian mixture model-based variational Bayesian approach (VBSDD-ETT) for solving the problem of skew-dense distribution (SDD) of measurement points in the ...

Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of ...

ABSTRACT: Precipitation is a critical meteorological factor that significantly impacts agriculture in the sub-Saharan and Sahelian regions of Africa. Accurate knowledge of precipitation levels aids in ...

What are Gaussian Mixture Models (GMM) useful for in the age of deep learning? GMMs might have come out of fashion for classification tasks, but they still have a few properties that make them useful ...

Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.