Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo

The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation for machine learning applications. The combined methodology that this research article pr...

Full description

Bibliographic Details

Published in:	Frontiers in Built Environment
Main Authors:	Anis Ben Abdessalem, Nikolaos Dervilis, David J. Wagg, Keith Worden
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2017-08-01
Subjects:	kernel selection hyperparameter estimation approximate Bayesian computation sequential Monte Carlo Gaussian processes
Online Access:	http://journal.frontiersin.org/article/10.3389/fbuil.2017.00052/full