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. For details about this covariance, 
see Ma and Bhadra (2019) at <a href="https://arxiv.org/abs/1911.05865">https://arxiv.org/abs/1911.05865</a>.

Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'.

Pulong Ma

GPBayes

Tools for Gaussian Process Modeling in Uncertainty
Quantification

CH function

<dl><dt>d</dt>
<dd>a matrix of distances</dd>
<dt>range</dt>
<dd>a numerical value containing the range parameter</dd>
<dt>tail</dt>
<dd>a numerical value containing the tail decay parameter</dd>
<dt>nu</dt>
<dd>a numerical value containing the smoothness parameter</dd></dl>

Arguments

Pulong Ma <a href="/link/mpulong%40gmail.com?package=GPBayes&version=0.1.0-5.1" data-mini-rdoc="GPBayes::mpulong@gmail.com">mpulong@gmail.com</a>

Author

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. For details about this covariance, 
see Ma and Bhadra (2019) at <a href='https://arxiv.org/abs/1911.05865'>https://arxiv.org/abs/1911.05865</a>.

The Confluent Hypergeometric correlation function proposed by Ma and Bhadra (2019) — CH

<dl>

<dt>d</dt>
<dd>a matrix of distances</dd>


<dt>range</dt>
<dd>a numerical value containing the range parameter</dd>


<dt>tail</dt>
<dd>a numerical value containing the tail decay parameter</dd>


<dt>nu</dt>
<dd>a numerical value containing the smoothness parameter</dd>

</dl>

Pulong Ma <a href='mailto:mpulong@gmail.com'>mpulong@gmail.com</a>

CH: The Confluent Hypergeometric correlation function proposed by Ma and Bhadra (2019)

Description

Usage

Value

Arguments

Author

See Also