Gaussian Process Regression (GPR)

版本 1.0.0 (1.8 MB) 作者: Kepeng Qiu
Gaussian Process Regression using GPML toolbox V4.2
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更新时间 2019/9/5
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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.

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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

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1.0.0

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