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Fundamentals of Bayesian Inference
Felipe Tobar

Mon Dec 06 09:05 AM -- 09:20 AM (PST) @

We revise the main components of Bayesian inference underpinning the treatment of the Gaussian process. Adopting an illustrative approach, we review statistical models, the approaches to learning the model parameters and the effect of the parameters’ cardinality.

Author Information

Felipe Tobar (Universidad de Chile)

Felipe Tobar is an Assistant Professor at the Data & AI Initiative at Universidad de Chile. He holds Researcher positions at the Center for Mathematical Modeling and the Advanced Center for Electrical Engineering. Felipe received the BSc/MSc degrees in Electrical Engineering (U. de Chile, 2010) and a PhD in Signal Processing (Imperial College London, 2014), and he was an Associate Researcher in Machine Learning at the University of Cambridge (2014-2015). Felipe teaches Statistics and Machine Learning courses at undergraduate, graduate and professional levels. His research interests lie in the interface between Machine Learning and Statistical Signal Processing, including Gaussian processes, spectral estimation, approximate inference, Bayesian nonparametrics, and optimal transport.

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