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We motivate the use of Gaussian random variables (RVs) from conceptual and practical perspectives. We examine the main properties of the Gaussian model and show how Gaussian RVs can be combined to construct Gaussian vectors
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.
More from the Same Authors
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2021 Poster: A novel notion of barycenter for probability distributions based on optimal weak mass transport »
Elsa Cazelles · Felipe Tobar · Joaquin Fontbona -
2021 : Concluding Remarks »
Felipe Tobar · César Lincoln Mattos -
2021 : Multioutput GPs »
Felipe Tobar -
2021 : Implementation of a GP »
Felipe Tobar -
2021 : Choosing the Kernel and Learning its Parameters »
Felipe Tobar -
2021 : Infinitely-many Gaussian RVs: The Gaussian process »
Felipe Tobar -
2021 : Fundamentals of Bayesian Inference »
Felipe Tobar -
2021 Tutorial: The Art of Gaussian Processes: Classical and Contemporary »
César Lincoln Mattos · Felipe Tobar -
2021 : Live Intro »
Felipe Tobar · César Lincoln Mattos -
2019 Poster: Band-Limited Gaussian Processes: The Sinc Kernel »
Felipe Tobar -
2018 Poster: Bayesian Nonparametric Spectral Estimation »
Felipe Tobar -
2018 Spotlight: Bayesian Nonparametric Spectral Estimation »
Felipe Tobar -
2017 Poster: Spectral Mixture Kernels for Multi-Output Gaussian Processes »
Gabriel Parra · Felipe Tobar -
2015 : Design of Covariance Functions using Inter-Domain Inducing Variables »
Felipe Tobar -
2015 Poster: Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels »
Felipe Tobar · Thang Bui · Richard Turner -
2015 Spotlight: Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels »
Felipe Tobar · Thang Bui · Richard Turner