plot

plot.gp

plot.gpvec

plot.dgp2

plot.dgp2vec

plot.dgp3

plot.dgp3vec

Acts on a <code>gp</code>, <code>gpvec</code>, <code>dgp2</code>, <code>dgp2vec</code>,
 <code>dgp3</code>, or <code>dgp3vec</code> object. 
 Generates trace plots for outer log likelihood, length scale,
 and nugget hyperparameters.
 Generates plots of hidden layers for one-dimensional inputs or monotonic
 warpings. Generates
 plots of the posterior mean and estimated 90% prediction intervals for 
 one-dimensional inputs; generates heat maps of the posterior mean and 
 point-wise variance for two-dimensional inputs.

Performs Bayesian posterior inference for deep Gaussian processes
following Sauer, Gramacy, and Higdon (2023, <doi:10.48550/arXiv.2012.08015>). See Sauer
(2023, <http://hdl.handle.net/10919/114845>) for comprehensive methodological
details and <https://bitbucket.org/gramacylab/deepgp-ex/> for a variety of
coding examples. Models are trained through MCMC including elliptical
slice sampling of latent Gaussian layers and Metropolis-Hastings
sampling of kernel hyperparameters. Vecchia-approximation for faster
computation is implemented following Sauer, Cooper, and Gramacy
(2023, <doi:10.48550/arXiv.2204.02904>). Optional monotonic warpings are implemented
following Barnett et al. (2024, <doi:10.48550/arXiv.2408.01540>). Downstream tasks include sequential design
through active learning Cohn/integrated mean squared error
(ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through
expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022 <doi:10.48550/arXiv.2112.07457>),
and contour location through entropy
(Booth, Renganathan, and Gramacy, 2024 <doi:10.48550/arXiv.2308.04420>). Models
extend up to three layers deep; a one layer model is equivalent to typical
Gaussian process regression. Incorporates OpenMP and SNOW parallelization
and utilizes C/C++ under the hood.

Annie Booth

deepgp

Bayesian Deep Gaussian Processes using MCMC

plot function

<dl><dt>x</dt>
<dd>object of class <code>gp</code>, <code>gpvec</code>, <code>dgp2</code>, 
<code>dgp2vec</code>, <code>dgp3</code>, or <code>dgp3vec</code></dd>
<dt>trace</dt>
<dd>logical indicating whether to generate trace plots (default is
TRUE if the object has not been through <code>predict</code>)</dd>
<dt>predict</dt>
<dd>logical indicating whether to generate posterior predictive 
plot (default is TRUE if the object has been through <code>predict</code>)</dd>
<dt>...</dt>
<dd>N/A</dd>
<dt>hidden</dt>
<dd>logical indicating whether to generate plots of hidden layers
(two or three layer only, default is FALSE)</dd></dl>

Arguments

Plots object from deepgp package — plot

<dl>

<dt>x</dt>
<dd>object of class <code>gp</code>, <code>gpvec</code>, <code>dgp2</code>, 
<code>dgp2vec</code>, <code>dgp3</code>, or <code>dgp3vec</code></dd>


<dt>trace</dt>
<dd>logical indicating whether to generate trace plots (default is
TRUE if the object has not been through <code>predict</code>)</dd>


<dt>predict</dt>
<dd>logical indicating whether to generate posterior predictive 
plot (default is TRUE if the object has been through <code>predict</code>)</dd>


<dt>...</dt>
<dd>N/A</dd>


<dt>hidden</dt>
<dd>logical indicating whether to generate plots of hidden layers
(two or three layer only, default is FALSE)</dd>

</dl>

plot: Plots object from `deepgp` package

Description

Usage

Arguments

Details

Examples