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Scalable and automated inference for gaussian process models

Nguyen, Trung Van


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)
DOI: 10.25911/5d5145e20cf5a


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