Abstract A Gaussian Mixture Model (GMM) is a parametric probability density function built
as a weighted sum of Gaussian distributions. Gaussian mixtures are used for modelling the …

Molecular communications have proven as a viable technology to enable information
exchange at the nanoscale, where conventional communication paradigms fail. However …

Basic oxygen furnace (BOF) steelmaking is a complicated physical chemical process, in
which the endpoint carbon content and temperature are two important indicators. In BOF …

Wind energy production depends not only on wind speed but also on wind direction. Thus,
predicting and estimating the wind direction for sites accurately will enhance measuring the …

This paper proposes a Bayesian nonparametric framework for clustering axially symmetric
data. Our approach is based on a Dirichlet processes mixture model with Watson …

In this article, we propose an effective mixture model-based approach to modeling and
clustering positive data vectors. Our mixture model is based on the inverted Beta-Liouville …

Due to the reason that spherical data (ie L 2 normalized vectors) are often involved with
various real-life applications (such as anomaly detection, gesture recognition, intrusion …

We propose two stochastic gradient MCMC methods for sampling from Bayesian posterior
distributions defined on Riemann manifolds with a known geodesic flow, eg hyperspheres …

In this work, we tackle the problem of clustering spherical (ie L 2 normalized) data vectors
using nonparametric Bayesian mixture models with von Mises distributions. Our model is …

Unsupervised learning and meta-learning share a common goal of enhancing learning
efficiency compared to starting from scratch. However, meta-learning methods are …