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

Frontiers in Built Environment

Wagg, David J.¹  Worden, Keith¹  Abdessalem, Anis Ben¹  Dervilis, Nikolaos¹ 

[1] Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, United Kingdom

关键词: kernel selection;    Hyperparameter estimation;    Approximate Bayesian Computation;    Sequential Monte Carlo;    Gaussian processes.;   

DOI  :  10.3389/fbuil.2017.00052

学科分类：建筑学

来源: Frontiers

【 摘 要 】

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 paper proposes and investigates offers the possibility to use different metrics and summary statistics of the kernels used for Bayesian regression. The presented work moves a step towards online, robust, consistent and automated mechanism to formulate optimal kernels (or even mean functions) and their hyperparameters simultaneously offering confidence evaluation when these tools are used for mathematical or engineering problems such as structural health monitoring (SHM) or system identification (SI).

【 授权许可】

CC BY