How does bayesopt fit a Gaussian process regression model to noisy data?

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James Finley 2019-1-15

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评论： Don Mathis 2019-1-17

Hi,

I am using bayesopt to optimise a non-deterministic objective function. I have set the ‘IsObjectiveDeterministic’ input argument to ‘false’, to reflect the stochastic nature of my objective function. My objective function features different levels of noise, depending on the input that is applied to the model.

My question is, does the Gaussian process regression model used in bayesopt assume a constant variance on the noise applied to objective function, or does the GPR model use a non-identically distributed noise for different data points in the observed data? If the latter case is true, how is the noise estimated for different inputs?

Many thanks

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Answer 1

Don Mathis 2019-1-16

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编辑：Don Mathis 2019-1-16

bayesopt uses fitrgp to fit the GP models, which assumes constant noise everywhere.

https://www.mathworks.com/help/stats/bayesian-optimization-algorithm.html

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James Finley 2019-1-17

Hi Don,

Many thanks for your response, you have have answered my question. I was also wondering how fitrgp estimates the variance for the noise in a non-determinisitc system?

Thank you

Don Mathis 2019-1-17