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Kernel Bayes Rule
Published on Jan 16, 20133578 Views
A nonparametric kernel-based method for realizing Bayes’ rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by t
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Chapter list
Kernel Bayes’ Rule: Nonparametric Bayesian inference with kernels00:00
Introduction00:03
Outline01:03
Kernel mean: representing probabilities - 101:26
Kernel mean: representing probabilities - 202:49
Characteristic kernel04:10
Nonparametric inference with kernels - 105:46
Nonparametric inference with kernels - 207:00
Conditional probabilities08:03
Conditional kernel mean - 108:10
Covariance09:07
Conditional kernel mean - 210:36
Conditional kernel mean - 312:30
Kernel Bayes’ Rule13:45
Inference with conditional kernel mean13:52
Kernel Sum Rule14:48
Kernel Chain Rule16:30
Kernel Bayes’ Rule (KBR)18:06
Inference with KBR - 119:52
Inference with KBR - 220:50
Example : KBR for nonparametric HMM - 122:47
Example : KBR for nonparametric HMM - 224:41
Example : KBR for nonparametric HMM - 325:41
Concluding remarks26:49
Ongoing / future works27:23
Collaborators28:41