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J Stat Softw. 2010 Jan 1;32(11). pii: i11.

Bayesian Functional Data Analysis Using WinBUGS.

Author information

1
Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe St. E3636, Baltimore, MD 21205, United States of America, URL: http://www.biostat.jhsph.edu/~ccrainic/

Abstract

We provide user friendly software for Bayesian analysis of functional data models using WinBUGS 1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.

PMID:
21743798
PMCID:
PMC3130307

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