CGGPappend

Add `batchsize` points to `SG` using `theta`.

Run computer experiments using the adaptive composite grid
algorithm with a Gaussian process model.
The algorithm works best when running an experiment that can evaluate thousands
of points from a deterministic computer simulation.
This package is an implementation of a forthcoming paper by Plumlee,
Erickson, Ankenman, et al. For a preprint of the paper,
contact the maintainer of this package.

Collin Erickson

CGGP

Composite Grid Gaussian Processes

Matthew Plumlee

CGGPappend function

<dl><dt>CGGP</dt>
<dd>Sparse grid object</dd>
<dt>batchsize</dt>
<dd>Number of points to add</dd>
<dt>selectionmethod</dt>
<dd>How points will be selected: one of `UCB`, `TS`,
`MAP`, `Oldest`, `Random`, or `Lowest`.
`UCB` uses Upper Confidence Bound estimates for the parameters.
`TS` uses Thompson sampling, a random sample from the posterior.
`MAP` uses maximum a posteriori parameter estimates.
`Oldest` adds the block that has been available the longest.
`Random` adds a random block.
`Lowest` adds the block with the lowest sum of index levels.
`UCB` and `TS` are based on bandit algorithms and account for uncertainty
in the parameter estimates, but are the slowest.
`MAP` is fast but doesn't account for parameter uncertainty.
The other three are naive methods that are not adaptive and won't
perform well.</dd></dl>

Arguments

Add points to CGGP — CGGPappend

<dl>

<dt>CGGP</dt>
<dd>Sparse grid object</dd>


<dt>batchsize</dt>
<dd>Number of points to add</dd>


<dt>selectionmethod</dt>
<dd>How points will be selected: one of `UCB`, `TS`,
`MAP`, `Oldest`, `Random`, or `Lowest`.
`UCB` uses Upper Confidence Bound estimates for the parameters.
`TS` uses Thompson sampling, a random sample from the posterior.
`MAP` uses maximum a posteriori parameter estimates.
`Oldest` adds the block that has been available the longest.
`Random` adds a random block.
`Lowest` adds the block with the lowest sum of index levels.
`UCB` and `TS` are based on bandit algorithms and account for uncertainty
in the parameter estimates, but are the slowest.
`MAP` is fast but doesn't account for parameter uncertainty.
The other three are naive methods that are not adaptive and won't
perform well.</dd>

</dl>

CGGPappend: Add points to CGGP

Description

Usage

Value

Arguments

See Also

Examples