Gaussian Process Regression for Functional Data Analysis
The main functions of the package are:
Gaussian process regression using stationary separable covariance kernels.
Gaussian process regression using nonstationary and/or nonseparable covariance kernels.
Multivariate Gaussian process – regression for multivariate outputs.
Functional regression model given by
where m is the m-th curve or surface; μ_m is from functional regression; and τ_m is from Gaussian Process regression with mean 0 covariance matrix k(\bf θ).
Jian Qing Shi, Yafeng Cheng, Evandro Konzen
Shi, J. Q., and Choi, T. (2011), “Gaussian Process Regression Analysis for Functional Data”, CRC Press.
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