PDF Estimation using Gaussian Mixtures

- Gaussian Mixtures
- Gaussian Mixtures and the E-M Algorithm

- Implementation Overview
- Implementation of the E-M algorithm :
`gmix_step.m`- Working in the log domain.
- Using the Cholesky Decomposition of
**.** - Choosing the covariance constraints
- Conditioning the Covariances

- Training
- Determining the number of modes.
- E-M algorithm (
`gmix_step.m`) - Pruning (
`gmix_deflate.m`) - Merging Modes (
`gmix_merge.m`) - Splitting modes (
`gmix_kurt.m`) - Convergence
- Training script (
`gmix_trainscript.m`) - Training on Huge data sets

- Conditional PDFs and Conditional
Mean using Gaussian Mixtures
- Conditional Estimation in general
- Estimation using Gaussian Mixtures
- MATLAB implementation
- Example of Estimation: Beam Interpolation
- CR Bound analysis

- An Example Script for Gaussian Mixtures

Baggenstoss 2017-05-19