Mixture models are probabilistic aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, nodes typically identified by their connectivity behaviour, with behaving similarly belonging to same community. this context, mixture modelling is pursued through stochastic blockmodelling. We consider blockmodels some variants extensions from persp...

Gaussian mixtures are a powerful and widely used tool to model non-Gaussian estimation problems. They able describe measurement errors that follow arbitrary distributions can represent ambiguity in assignment tasks like point set registration or tracking. However, using them with common least squares solvers is still difficult. Existing approaches either approximations of the true mixture prone...

Abstract This article presents basic ideas of finite mixture models in which the number components is known and distributions comprising are not assumed to come from any parametrically specified family. categorized under: Algorithms Computational Methods > Statistical Learning Exploratory Data Sciences Clustering Classification Graphical Analysis Nonparametric Models