The 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)

Shrinkage Estimator for Bayesian Network Parametrs

author: John Burge, University of New Mexico

Categories

Top: Computer Science: Machine Learning: Bayesian Learning

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*Slides*
0:00	Shrinkage Estimator for Bayesian Network Parameters
0:09	Outline
0:28	High-Level Overview
1:06	Neuroimaging Application (1)
1:44	Neuroimaging Application (2)
2:31	Find Correlations Among RVs
3:08	Model with Bayesian Networks
4:20	Bayesian Network Model Selection
5:34	Parameterizing Bayesian Networks
6:35	Laplacian Smoothing (1)
7:02	Laplacian Smoothing (2)
7:25	Shrinkage
8:52	ROI Hierarchy
10:24	How Much to Smooth?
11:02	Calculating Mixture Weights (1)
12:27	Calculating Mixture Weights (2)
13:23	Mixing Weights for Neuroimaging Data
15:06	Results
15:39	Simulated Data Results Laplacian Smoothing Constant
16:28	Results
17:05	Results: Likelihood of Left-Out Data (1)
17:50	Results: Likelihood of Left-Out Data (2)
18:56	Conclusions
20:04	Acknowledgements
20:14	Thank You!

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