Bayesian mixture models for complex high dimensional count data in phage display experiments

Journal article

Yuan Ji, Guosheng Yin, Kam-Wah Tsui, Mikhail G Kolonin, Jessica Sun, Wadih Arap, Renata Pasqualini and Kim-Anh Do

Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol.56(2), pp.139-152

Received February 2006. Revised October 2006

03/2007

Abstract

Gibbs sampler

Metropolis-Hastings algorithm

Bayesian inference

Markov chain Monte Carlo simulation

Peptide

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Title: Bayesian mixture models for complex high dimensional count data in phage display experiments
Creators: Yuan Ji - University of Texas M. D. Anderson Cancer Center, Houston, USA
Guosheng Yin - University of Texas M. D. Anderson Cancer Center, Houston, USA
Kam-Wah Tsui - University of Wisconsin-Madison, USA
Mikhail G Kolonin - University of Texas M. D. Anderson Cancer Center, Houston, USA
Jessica Sun - University of Texas M. D. Anderson Cancer Center, Houston, USA
Wadih Arap - University of Texas M. D. Anderson Cancer Center, Houston, USA
Renata Pasqualini - University of Texas M. D. Anderson Cancer Center, Houston, USA
Kim-Anh Do - University of Texas M. D. Anderson Cancer Center, Houston, USA
Publication Details: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol.56(2), pp.139-152
Date published: 03/2007
Publisher: Blackwell Publishing Ltd
Edition: Received February 2006. Revised October 2006
Number of pages: 14
Academic Unit: Radiation Oncology; Cancer Institute of New Jersey at University Hospital
Language: English
Resource Type: Journal article
Identifiers: 991031654533404646