gpR-package: gpR
In dirmeier/gpR: Gaussian processes in R
Description
Author(s)
References
Description
gpR is a Bayesian machine learning package using latent Gaussian processes.
In general supervised machine learning can be divided in classification,
where we describe data using discrete labels, and regression, where the labels are continuous.
Author(s)
Simon Dirmeier | simon.dirmeier@gmx.de
References
Rasmussen C.E. and Williams C.K.I. (2006), Gaussian Processes for Machine Learning, MIT Press
http://www.gaussianprocess.org/gpml/
Barber D. (2015), Bayesian Reasoning and Machine Learning, Cambridge University Press
http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online
dirmeier/gpR documentation built on May 15, 2019, 8:50 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Author(s) References
Description
gpR is a Bayesian machine learning package using latent Gaussian processes. In general supervised machine learning can be divided in classification, where we describe data using discrete labels, and regression, where the labels are continuous.
Author(s)
Simon Dirmeier | simon.dirmeier@gmx.de
References
Rasmussen C.E. and Williams C.K.I. (2006), Gaussian Processes for Machine Learning, MIT Press
http://www.gaussianprocess.org/gpml/
Barber D. (2015), Bayesian Reasoning and Machine Learning, Cambridge University Press
http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.