Projection predictive model selection for Gaussian processes

We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results on synthetic and real world datasets show that the proposed m...

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Bibliographic Details
Published in:arXiv.org 2016-08
Main Authors: Piironen, Juho, Vehtari, Aki
Format: Article
Language:English
Subjects:
Forecasting
Gaussian process
Mathematical models
Prediction models
Online Access:Get full text
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Summary:We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results on synthetic and real world datasets show that the proposed method improves the assessment of variable relevance compared to the automatic relevance determination (ARD) via the length-scale parameters. We expect the method to be useful for improving explainability of the models, reducing the future measurement costs and reducing the computation time for making new predictions.
ISSN:2331-8422
DOI:10.48550/arxiv.1510.04813