Marginalizing Gaussian process hyperparameters using sequential Monte Carlo
2015 (English)In: Proceedings of the 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Institute of Electrical and Electronics Engineers (IEEE), 2015, 477-480 p.Conference paper (Refereed)
Gaussian process regression is a popular method for non-parametric probabilistic modeling of functions. The Gaussian process prior is characterized by so-called hyperparameters, which often have a large influence on the posterior model and can be difficult to tune. This work provides a method for numerical marginalization of the hyperparameters, relying on the rigorous framework of sequential Monte Carlo. Our method is well suited for online problems, and we demonstrate its ability to handle real-world problems with several dimensions and compare it to other marginalization methods. We also conclude that our proposed method is a competitive alternative to the commonly used point estimates maximizing the likelihood, both in terms of computational load and its ability to handle multimodal posteriors.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 477-480 p.
Control Engineering Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-127712DOI: 10.1109/CAMSAP.2015.7383840ISI: 000380473300124ISBN: 978-1-4799-1963-5OAI: oai:DiVA.org:liu-127712DiVA: diva2:926829
The 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 13-16, 2015
FunderSwedish Research Council, 621-2013-5524