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Author(s):
A. Lederer; J. Umlauft; S. Hirche 
Title:
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves 
Abstract:
The posterior variance of Gaussian processes is a valuable measure of the learning error which is exploited in various applications such as safe reinforcement learning and control design. However, suitable analysis of the posterior variance which captures its behavior for finite and infinite number of training data is missing. This paper derives a novel bound for the posterior variance function which requires only local information because it depends only on the number of training samples in the...    »
 
Keywords:
conhumo; data_driven_control 
Published as:
arXiv preprint: arXiv:1906.01404 
Month:
Jun 
Year:
2019