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Probabilistic and Bayesian Modelling I
Published on Feb 25, 200717482 Views
There is a dramatic growth in the availability of complex data from a wide range of different applications. The challenge of the data analyzer is to extract knowledge from the raw data by identifying
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Chapter list
Probabilistic & Bayesian Modeling00:01
Overview02:41
Simple example: The biased coin04:01
Exmple II: Gaussian density13:56
Example III: Gaussian noise and Linear Regression20:12
Example III: Gaussian noise and Linear Regression (cont.)29:50
Properties of Estimators31:14
Properties of Estimators (diagrams)36:14
Properties of Estimators (cont)43:51
Example: Independent Component Analysis48:08
Generative Model50:05
Generative Model (cont.)55:41
Feature Extraction59:15
Compute the Likelihood01:00:38