Skip navigation
Skip navigation

Scalable and automated inference for gaussian process models

Nguyen, Trung Van

Description

Gaussian processes (GPs) are widely used in the Bayesian approach to supervised learning. Their ability to provide rich priors over functions is highly desirable for modeling real-world problems. Unfortunately, there exist two big challenges when doing Bayesian inference (i.e., learning the posteriors over functions) for GP models. The first is analytical intractability: The posteriors cannot be computed in closed- form when non-Gaussian likelihoods are employed. The second is scalability: The...[Show more]

CollectionsOpen Access Theses
Date published: 2015
Type: Thesis (PhD)
URI: http://hdl.handle.net/1885/156220
DOI: 10.25911/5d5145e20cf5a

Download

File Description SizeFormat Image
b38071964_Nguyen_Trung V..pdf294.57 MBAdobe PDFThumbnail


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  22 January 2019/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator