CH: The Confluent Hypergeometric correlation function proposed by...

In GPBayes: Tools for Gaussian Process Modeling in Uncertainty Quantification

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; \Sexpr[results=rd]{tools:::Rd_expr_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 [e-mail]

See Also

GPBayes-package, GaSP, gp, matern, kernel, ikernel

GPBayes documentation built on May 29, 2024, 7:30 a.m.