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Gaussian Process: Practical Course
Published on Jan 15, 20138934 Views
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Chapter list
Gaussian Process: Practical Course00:00
GPML Toolbox by C.E. Rasmussen and H. Nickisch00:00
GPML Toolbox: Overview00:49
GPML Toolbox: Supporting structures and functions02:09
GPML Toolbox: Specifying model properties04:35
Inference Methods05:46
Likelihood functions08:43
GPML Toolbox: Compatibility matrix09:31
Mean functions11:04
Covariance functions: Simple12:07
Covariance functions: Composite12:08
The gp function: Overview - 112:18
The gp function: Overview - 218:33
The gp function: Overview - 319:31
The gp function: Process input arguments20:15
The gp function: Check & initialize hyperparameters20:56
The gp function: Do inference – issue a warning if it fails in training mode & try to recover21:41
The gp function: Compute test predictions23:01
The gp function: Make predictions – for all test points in a mini-batch24:07
Inference Methods24:47
Marginal Likelihood vs Cross Validation25:33