en-de
en-es
en-fr
en-sl
en
en-zh
0.25
0.5
0.75
1.25
1.5
1.75
2
Why Bayesian nonparametrics?
Published on Jan 24, 201223617 Views
Related categories
Chapter list
Why Bayesian nonparametrics?00:00
Why...01:13
Bayesian Machine Learning02:06
Modeling vs toolbox views of Machine Learning (and Statistics)08:21
Parametric vs Nonparametric Models10:49
Why nonparametrics?13:14
Desirable properties of ML methods16:09
Gaussian and Dirichlet Processes24:37
Coverage of Priors27:12
Applications30:41
Outline33:55
Sparse Matrices34:52
From finite to in finite sparse binary matrices35:17
Properties of the Indian bu ffet process38:35
From binary to non-binary latent features39:32
Posterior Inference in IBPs40:09
Modelling Data with Indian Bu ffet Processes40:47
Nonparametric Binary Matrix Factorization41:48
Learning Structure of Deep Sparse Graphical Models - 143:20
Learning Structure of Deep Sparse Graphical Models - 244:26
Learning Structure of Deep Sparse Graphical Models - 344:29
Learning Structure of Deep Sparse Graphical Models - 444:32
Learning Structure of Deep Sparse Graphical Models - 545:00
Learning Structure of Deep Sparse Graphical Models - 646:01
Reality Check...46:30
Success Stories... not mine :-)48:30
Motion Capture Segmentation49:03
Word Segmentation49:31
Language Modelling and Compression49:54
A comparison of Gaussian Process Classi cation and SVMs50:32
Covariance Matrices - 152:40
Covariance Matrices - 253:02
Generalised Wishart Processes for Covariance modelling53:41
Generalised Wishart Process Results - 155:02
Generalised Wishart Process Results - 255:30
Gaussian process regression networks55:32
Gaussian process regression networks: properties57:01
Gaussian process regression networks: results - 157:13
Gaussian process regression networks: results - 257:22
Summary57:32
Discussion / Critique58:33