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Fully Bayesian Source Separation with Application to the CMB
Published on Feb 15, 20084173 Views
Blind source separation refers to the inferring of the values of variables (known as sources) from observations that are linear combinations of them. The observations and sources are usually vectors.
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Chapter list
E-Team on Unsupervised Segmentation Fully Bayesian Source Separation with Application to the Cosmic Microwave Background00:00
The Problem - Factor Analysis00:25
Cosmic Microwave Background (CMB)01:11
CMB Spectrum - Black Body02:09
Inferring the CMB - Source Separation02:23
Separating the Cosmic Microwave Background03:05
Model04:37
Model for Sources05:03
Model for Mixing Matrix A - 106:04
Model for Mixing Matrix A - 207:37
Priors08:45
Sampling from the Posterior Distribution09:40
Example 1: Simulated Data - 110:43
Example 1: Simulated Data - 211:59
Example 1: Simulated Data - 312:16
Example 1: Simulated Data - 412:30
Example 1: Simulated Data - 512:52
Example 1: Simulated Data - 613:28
Example 1: Simulated Data - 713:55
Example 1: Simulated Data - 814:23
Example 1: Simulated Data - 914:41
Example 2: Simulated Data with 9 Channels - 114:41
Example 2: Simulated Data with 9 Channels - 215:13
Example 2: Simulated Data with 9 Channels - 315:24
Example 2: Simulated Data with 9 Channels - 415:25
Example 2: Simulated Data with 9 Channels - 515:30
Example 2: Simulated Data with 9 Channels - 615:48
Real WMAP Data15:49
Patch 2 - Data16:50
Patch 2 - Posterior Mean of Sources17:12
Patch 2 - Posterior Standard Deviation of Sources17:27
Patch 2 - Model Fit: Observed Temperature vs. Posterior Mean Temperature17:48
Patch 3 - Data18:28
Patch 3 - Posterior Mean of Sources18:35
Patch 3 - Posterior Standard Deviation of Sources18:43
Patch 3 - Model Fit: Observed Temperature vs. Posterior Mean Temperature18:52
Posterior Mean of Spectral Indices18:59
Future Work19:39