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Bayesian Learning of Finite Asymmetric Gaussian Mixtures

  • Shuai Fu
  • Nizar Bouguila
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

Asymmetric Gaussian mixture (AGM) model has been proven to be more flexible than the classic Gaussian mixture model from many aspects. In contrast with previous efforts that have focused on maximum likelihood estimation, this paper introduces a fully Bayesian learning approach using Metropolis-Hastings (MH) within Gibbs sampling method to learn AGM model. We show the merits of the proposed model using synthetic data and a challenging intrusion detection application.

Keywords

Asymmetric Gaussian mixture Metropolis-Hastings Gibbs sampling MCMC Intrusion detection 

Notes

Acknowledgment

The completion of this research work was made possible thanks to Concordia University via a Concordia University Research Chair Tier II.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Concordia UniversityMontrealCanada

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