GP.std.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.std.grids function

<dl><dt>grids</dt>
<dd>A matrix where rows represent grid points and columns are coordinates.</dd>
<dt>center</dt>
<dd>A vector of real numbers specifying the centroid parameters in the modified exponential squared kernel. The default value is NULL and set to the center of the grid points: apply(x,2,mean).</dd>
<dt>scale</dt>
<dd>A vector of positive numbers specifying the scale parameters in the modified exponential squared kernel. The default value is NULL and set to values such that grid points in a range of (-max_range,max_range) in each dimension.</dd>
<dt>max_range</dt>
<dd>A positive real number indicating the maximum range of the grid points to specify the scale parameter. The default value is NULL and set to 6.</dd></dl>

Arguments

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

Author

Compute the standardized grids — GP.std.grids

<dl>

<dt>grids</dt>
<dd>A matrix where rows represent grid points and columns are coordinates.</dd>


<dt>center</dt>
<dd>A vector of real numbers specifying the centroid parameters in the modified exponential squared kernel. The default value is NULL and set to the center of the grid points: apply(x,2,mean).</dd>


<dt>scale</dt>
<dd>A vector of positive numbers specifying the scale parameters in the modified exponential squared kernel. The default value is NULL and set to values such that grid points in a range of (-max_range,max_range) in each dimension.</dd>


<dt>max_range</dt>
<dd>A positive real number indicating the maximum range of the grid points to specify the scale parameter. The default value is NULL and set to 6.</dd>

</dl>

GP.std.grids: Compute the standardized grids

Description

Usage

Value

Arguments

Author

Examples