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No voodoo here! Learning discrete graphical models via inverse covariance estimation
Published on Feb 4, 20256442 Views
We investigate the relationship between the support of the inverses of generalized covariance matrices and the structure of a discrete graphical model. We show that for certain graph structures, the s
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Presentation
No voodoo here! Learning discrete graphical models via inverse covariance estimation00:00
Introduction: Graphical models (1)00:22
Introduction: Graphical models (2)00:45
Introduction: Graphical models (3)01:02
Introduction: Graphical models (4)01:14
Introduction: Graphical models (5)01:38
Structure learning for Gaussians (1)02:01
Structure learning for Gaussians (2)02:41
Non-Gaussian distributions (1)03:49
Non-Gaussian distributions (2)04:02
Non-Gaussian distributions (3)04:28
A curious example (1)04:38
A curious example (2)04:55
A curious example (3)05:04
A curious example (4)05:10
A curious example (5)05:26
A curious example (6)06:15
A curious example (7)06:23
A curious example (8)06:53
Some notation (1)07:56
Some notation (2)08:13
Some notation (3)08:44
Some notation (4)09:05
Augmenting covariance matrix (1)09:22
Augmenting covariance matrix (2)09:32
Augmenting covariance matrix (3)09:45
Augmenting covariance matrix (4)10:08
Augmenting covariance matrix (5)10:45
Example: Binary Ising model (1)10:50
Example: Binary Ising model (2)11:07
Consequences for trees (1)12:03
Consequences for trees (2)12:13
Consequences for trees (3)12:26
Structure learning (1)12:44
Structure learning (2)13:04
Structure learning (3)13:43
Graphical Lasso (1)13:55
Graphical Lasso (2)14:30
Graphical Lasso (3)14:58
Simulation study15:23
Inference methods for non-trees (1)15:51
Inference methods for non-trees (2)16:20
Inference methods for non-trees (3)16:24
Summary (1)16:34
Summary (2)16:51
Summary (3)16:56
Summary (4)17:07