## Gaussian Processes

author: Carl Edward Rasmussen, Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Nov. 2, 2009,   recorded: September 2009,   views: 5239
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# Slides

0:00 Slides Solving Challenging Non-linear Regression Problems by Manipulating a Gaussian Distribution The Prediction Problem (1) The Prediction Problem (2) The Prediction Problem (3) The Prediction Problem (4) Maximum likelihood, parametric model Bayesian Inference, parametric model Bayesian Inference, parametric model, cont. Bayesian Inference, parametric model The Gaussian Distribution Conditionals and Marginals of a Gaussian Conditionals and Marginals of a Gaussian What is a Gaussian Process? The marginalization property Random functions from a Gaussian Process Some values of the random function Random functions from a Gaussian Process Joint Generation Sequential Generation Function drawn at random from a Gaussian Process with Gaussian covariance Maximum likelihood, parametric model Bayesian Inference, parametric model Bayesian Inference, parametric model, cont. Non-parametric Gaussian process models Prior and Posterior Graphical model for Gaussian Process Some interpretation The marginal likelihood Example: Fitting the length scale parameter Why, in principle, does Bayesian Inference work? Occam’s Razor Example: Fitting the length scale parameter The marginal likelihood An illustrative analogous example Solving Challenging Non-linear Regression Problems by Manipulating a Gaussian Distribution

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