Estimates from Gaussian Process regression (function: `fitgpr`) for given set of hyperparameter

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I am interested in estimating y using Gaussian Process for given hyperparameters and noise parameter i.e. without optimizing for parameters.
In the following example; [3.5, 6.2, 0.2] are provided as initial guess parameters,
load(fullfile(matlabroot,'examples','stats','gprdata2.mat'))
sigma0 = 0.2;
kparams0 = [3.5, 6.2];
gprMdl2 = fitrgp(x,y,'KernelFunction','squaredexponential',...
'KernelParameters',kparams0,'Sigma',sigma0);
ypred2 = resubPredict(gprMdl2);
But I am interested in seeing model's response y and other properties (like: loglikelihood) precisely for parameters [3.5, 6.2, 0.2] not for optimized ones.
Thanks

Akzeptierte Antwort

Gautam Pendse
Gautam Pendse am 20 Mai 2017
Hi Pankaj,
You probably want to use 'FitMethod','none' in the call to fitrgp. For more info, have a look at the doc for 'FitMethod':
https://www.mathworks.com/help/stats/fitrgp.html#namevaluepairarguments
Hope this helps,
Gautam

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