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Modern Bayesian Nonparametrics: beyond Dirichlet and Gaussian processes
Published on Jan 16, 20138799 Views
Nonparametrics plays an important role in Bayesian modelling: nonparametric models are flexible, realistic and by providing good coverage can guard against model inadequacy. Modern Bayesian nonpar
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Chapter list
Modern Bayesian Nonparametrics: Beyond Dirichlet and Gaussian processes00:00
Probabilistic Modelling01:41
Bayesian Modelling03:43
Learning Model Structure06:45
Bayesian Nonparametrics08:18
Why...08:30
Parametric vs Nonparametric Models09:39
Why nonparametrics?11:33
Examples of non-parametric models13:50
Gaussian and Dirichlet Processes15:31
Gaussian Processes and SVMs17:31
Support Vector Machines and Gaussian Processes17:46
A picture21:50
Moving beyond GPs and DPs23:54
Sparse binary matrices and the Indian bu et process24:52
Nonparametric Binary Matrix Factorization27:40
The Big Picture: Relations between some models28:47
Networks30:20
Modelling Networks30:38
Latent Class Models - 131:07
Latent Class Models - 231:47
Nonparametric Latent Class Models32:04
Latent Feature Models32:59
In nite Latent Attribute model for network data - 134:25
In nite Latent Attribute model for network data - 236:01
In nite Latent Attribute: Results36:37
Exchangeable Arrays37:47
Random Function Model - 140:58
Random Function Model - 241:47
Random Function Model: Results42:30
Covariance Matrices45:07
Generalised Wishart Processes for Covariance modelling45:52
Hierarchies48:18
Dirichlet Di usion Trees (DDT) - 150:43
Dirichlet Di usion Trees (DDT) - 251:10
Pitman-Yor Di usion Trees52:36
Summary53:09