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

4 views (last 30 days)
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,
sigma0 = 0.2;
kparams0 = [3.5, 6.2];
gprMdl2 = fitrgp(x,y,'KernelFunction','squaredexponential',...
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.

Accepted Answer

Gautam Pendse
Gautam Pendse on 20 May 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':
Hope this helps,

More Answers (0)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!