Bayesian Approaches to Gaussian Mixture Modeling

13 years 4 months ago

Download www.bioss.ac.uk

—A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an “optimal” number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results. The methods are tested on synthetic and real data sets.

Stephen J. Roberts, Dirk Husmeier, Iead Rezek, Wil

Real-time Traffic

Data Sets | Gaussian Mixture Model | PAMI 1998 | —a Bayesian-based Methodology |

claim paper

» Bayesian speaker recognition using Gaussian mixture model and laplace approximation

» Robust Bayesian Mixture Modelling

» Parameterbased reduction of Gaussian mixture models with a variationalBayes approach

» Parsimonious reduction of Gaussian mixture models with a variationalBayes approach

» Kernel Trick Embedded Gaussian Mixture Model

» An Unsupervised Gaussian Mixture Classification Mechanism Based on Statistical Learning An...

» Probabilistic Community Discovery Using Hierarchical Latent Gaussian Mixture Model

» Unsupervised Selection and Discriminative Estimation of Orthogonal Gaussian Mixture Models...

Post Info
More Details (n/a)

Added	23 Dec 2010
Updated	23 Dec 2010
Type	Journal
Year	1998
Where	PAMI
Authors	Stephen J. Roberts, Dirk Husmeier, Iead Rezek, William D. Penny

Comments (0)

Sciweavers

Bayesian Approaches to Gaussian Mixture Modeling

Data Sets | Gaussian Mixture Model | PAMI 1998 | —a Bayesian-based Methodology |

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers