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Gaussian Process Regression (GPR)

version 1.0.0 (1.81 MB) by Kepeng Qiu
Gaussian Process Regression using GPML toolbox V4.2

791 Downloads

Updated 05 Sep 2019

From GitHub

View license on GitHub

1. This code is based on the GPML toolbox V4.2.
2. Provided two demos (multiple input single output & multiple input multiple output).
3. Use feval(@ function name) to see the number of hyperparameters in a function. For example:
K > > feval (@ covRQiso)
Ans =
'(1 + 1 + 1)'
It shows that the covariance function covRQiso requires 3 hyperparameters. Therefore, 3
hyperparameters need to be initialized when using the optimization function minimize. The meaning
and range of each hyperparameter are explained in detail in the description of each function.

4. Different likelihood functions have different inference function requirements, which can be seen in
detail ./gpml-matlab-v4.2-2018-06-11/doc/index.html or ./gpml-matlab-v4.2-2018-06-
11/doc/manual.PDF.

MATLAB Release Compatibility
Created with R2019a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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func

gpml-matlab-v4.2-2018-06-11

gpml-matlab-v4.2-2018-06-11/cov

gpml-matlab-v4.2-2018-06-11/doc

gpml-matlab-v4.2-2018-06-11/inf

gpml-matlab-v4.2-2018-06-11/lik

gpml-matlab-v4.2-2018-06-11/mean

gpml-matlab-v4.2-2018-06-11/prior

gpml-matlab-v4.2-2018-06-11/util

gpml-matlab-v4.2-2018-06-11/util/minfunc

gpml-matlab-v4.2-2018-06-11/util/minfunc/mex

gpml-matlab-v4.2-2018-06-11/util/sparseinv

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.