vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R | Zendy

Nicola Lama | Zendy; Mark Girolami | Zendy

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vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R

Author(s) -

Nicola Lama,

Mark Girolami

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm535

Subject(s) - computer science , bioconductor , multinomial probit , weighting , prior probability , bayesian probability , multinomial distribution , gaussian process , artificial intelligence , pattern recognition (psychology) , posterior probability , multinomial logistic regression , data mining , software , class (philosophy) , gaussian , mathematics , machine learning , statistics , medicine , biochemistry , chemistry , physics , radiology , quantum mechanics , gene , programming language

Vbmp is an R package for Gaussian Process classification of data over multiple classes. It features multinomial probit regression with Gaussian Process priors and estimates class posterior probabilities employing fast variational approximations to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. Being equipped with only one main function and reasonable default values for optional parameters, vbmp combines flexibility with ease of usage as is demonstrated on a breast cancer microarray study.

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