A comparison of Gaussian processes and neural networks for computer model emulation and calibration

Author(s):  
Samuel Myren ◽  
Earl Lawrence
Technometrics ◽  
2021 ◽  
pp. 1-36
Author(s):  
Pulong Ma ◽  
Anirban Mondal ◽  
Bledar A. Konomi ◽  
Jonathan Hobbs ◽  
Joon Jin Song ◽  
...  

1998 ◽  
Vol 10 (5) ◽  
pp. 1203-1216 ◽  
Author(s):  
Christopher K. I. Williams

For neural networks with a wide class of weight priors, it can be shown that in the limit of an infinite number of hidden units, the prior over functions tends to a gaussian process. In this article, analytic forms are derived for the covariance function of the gaussian processes corresponding to networks with sigmoidal and gaussian hidden units. This allows predictions to be made efficiently using networks with an infinite number of hidden units and shows, somewhat paradoxically, that it may be easier to carry out Bayesian prediction with infinite networks rather than finite ones.


2020 ◽  
Vol 138 ◽  
pp. 75-81
Author(s):  
Arnu Pretorius ◽  
Herman Kamper ◽  
Steve Kroon

2015 ◽  
Author(s):  
Tamás Grósz ◽  
Róbert Busa-Fekete ◽  
Gábor Gosztolya ◽  
László Tóth

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