Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data

Aijun Yang; Yuzhu Tian; Yunxian Li; Jin‐Guan Lin

doi:10.1007/s00180-019-00917-8

ScienceGate Book Chapters

JOURNAL ARTICLE

Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data

Aijun Yang Yuzhu Tian Yunxian Li Jin‐Guan Lin

Year: 2019 Journal: Computational Statistics Vol: 35 (1)Pages: 245-258 Publisher: Springer Science+Business Media

DOI: 10.1007/s00180-019-00917-8

Get Full-Text PDF Get Analytical Report

Keywords:

Kernel (algebra) Bayesian linear regression Bayesian probability Feature selection Posterior probability Probit model Mathematics Markov chain Monte Carlo Computer science Artificial intelligence Pattern recognition (psychology) Multinomial probit Bayesian inference Statistics

Metrics

Cited By

0.31

FWCI (Field Weighted Citation Impact)

Refs

0.65

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Sparse Bayesian variable selection in kernel probit model for analyzing high-dimensional data

Metrics

Citation History

Topics

Related Documents

Sparse bayesian kernel multinomial probit regression model for high-dimensional data classification

Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Bayesian variable selection in multinomial probit model for classifying high-dimensional data

Sparse Bayesian variable selection for classifying high-dimensional data

Model Identification and Variable Selection for High-Dimensional Sparse Data