| CH {GPBayes} | R Documentation |
The Confluent Hypergeometric correlation function proposed by Ma and Bhadra (2023)
Description
This function computes the Confluent Hypergeometric correlation function given a distance matrix. The Confluent Hypergeometric correlation function is given by
C(h) = \frac{\Gamma(\nu+\alpha)}{\Gamma(\nu)}
\mathcal{U}\left(\alpha, 1-\nu, \biggr(\frac{h}{\beta}\biggr)^2 \right),
where \alpha is the tail decay parameter. \beta is the range parameter.
\nu is the smoothness parameter. \mathcal{U}(\cdot) is the confluent hypergeometric
function of the second kind. Note that this parameterization of the CH covariance
is different from the one in Ma and Bhadra (2023). For details about this covariance,
see Ma and Bhadra (2023; doi:10.1080/01621459.2022.2027775).
Usage
CH(d, range, tail, nu)
Arguments
d |
a matrix of distances |
range |
a numerical value containing the range parameter |
tail |
a numerical value containing the tail decay parameter |
nu |
a numerical value containing the smoothness parameter |
Value
a numerical matrix
Author(s)
Pulong Ma mpulong@gmail.com
See Also
GPBayes-package, GaSP, gp, matern, kernel, ikernel