Bayesian Optimization Results Evaluation
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MByk
am 23 Mai 2018
Bearbeitet: muhamed ibrahim
am 30 Aug. 2019
I am trying to learn and understand Bayesian Optimization. My code is working like in the documentation page but what is the difference between best observed feasible point and best estimated feasible point? Which result should I consider? Thanks for the help.
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Alan Weiss
am 24 Mai 2018
The difference is that the algorithm makes a model of the objective function, and this model assumes that observations can contain noise (errors). So the best observed feasible point is the one with the lowest returned value from objective function evaluations. The best estimated feasible point is the one that has the lowest estimated mean value according to the latest model of the objective function.
If your objective function is deterministic, then you can set the 'IsObjectiveDeterministic' name-value pair to true, and then these two points are likely to coincide.
Alan Weiss
MATLAB mathematical toolbox documentation
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Alan Weiss
am 19 Aug. 2019
To stop an optimization early, use the OutputFcn name-value pair. For details, see Bayesian Optimization Output Functions.
Alan Weiss
MATLAB mathematical toolbox documentation
muhamed ibrahim
am 30 Aug. 2019
Bearbeitet: muhamed ibrahim
am 30 Aug. 2019
regardin what you typed "and this model assumes that observations can contain noise (errors).
"How does Matlab compute this amount of noise? is it an arbitarary value?
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