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Vbmp: variational Bayesian Multinomial Probit Regression for multi-class classification in R.

Nicola Lama¹, Mark Girolami

¹Medical Statistics Unit, Department of Medicine and Public Health, Second University of Napoli, Italy. nicola.lama@unina2.it

Bioinformatics (Oxford, England)

|November 16, 2007

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The vbmp R package offers Gaussian Process classification for multi-class data using multinomial probit regression. It provides efficient posterior probability estimation and feature weighting for enhanced usability in biological studies.

Area of Science:

Computational biology
Machine learning
Statistical modeling

Background:

The R package vbmp implements a novel method for Gaussian Process classification.
This method is available as part of the Bioconductor project.

Purpose of the Study:

To introduce and describe the vbmp R package for multi-class Gaussian Process classification.
To demonstrate the package's flexibility and ease of use on a real-world dataset.

Main Methods:

Utilizes multinomial probit regression with Gaussian Process priors.
Employs fast variational approximations for estimating class posterior probabilities.
Incorporates Automatic Relevance Determination for feature weighting.

Main Results:

Related Experiment Videos

The vbmp package provides an efficient and user-friendly tool for complex classification tasks.
Demonstrated effectiveness on a breast cancer microarray dataset, showcasing its practical application.

Conclusions:

Vbmp is a flexible and easy-to-use R package for multi-class Gaussian Process classification.
The package combines advanced statistical methods with practical implementation for biological data analysis.