Scalable and automated inference for gaussian process models
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]
|Collections||Open Access Theses|
|b38071964_Nguyen_Trung V..pdf||294.57 MB||Adobe PDF|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.