GP.generate.grids

Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.

Jian Kang

BayesGPfit

Fast Bayesian Gaussian Process Regression Fitting

John Burkardt

GP.generate.grids function

<dl><dt>d</dt>
<dd>An integer number for the dimension of the space. The default value is 1.</dd>
<dt>num_grids</dt>
<dd>An integer number for the number of grids in each dimension. The default value is 50.</dd>
<dt>grids_lim</dt>
<dd>A vector of two real numbers for the range of the grids in each dimension. The default value is c(-1,1).</dd>
<dt>random</dt>
<dd>A logical value indicating whether each dimension of the grids is generated from a uniform distribution or fixed as equally-spaced.</dd></dl>

Arguments

Jian Kang &lt;jiankang@umich.edu&gt;

Author

Create spatial grids. — GP.generate.grids

<dl>

<dt>d</dt>
<dd>An integer number for the dimension of the space. The default value is 1.</dd>


<dt>num_grids</dt>
<dd>An integer number for the number of grids in each dimension. The default value is 50.</dd>


<dt>grids_lim</dt>
<dd>A vector of two real numbers for the range of the grids in each dimension. The default value is c(-1,1).</dd>


<dt>random</dt>
<dd>A logical value indicating whether each dimension of the grids is generated from a uniform distribution or fixed as equally-spaced.</dd>

</dl>

GP.generate.grids: Create spatial grids.

Description

Usage

Value

Arguments

Author

Examples