PDF Estimation using Gaussian Mixtures

This section is concerned with the general PDF estimation problem. Let $p({\bf z})$ be the PDF of ${\bf z}$ which must be estimated from training samples. If $p({\bf z})$ is continuous, it may be approximated to arbitrary accuracy by any kernel-based estimator [59], such as the method of Gaussian Mixtures (GM) [60] given enough terms.