Gaussian Model with Non-Linear Parameter Dependence

In many cases, the data may depend non-linearly on a parameter. If the feature extraction (signal processing) involves estimating this parameter, it may require an iterative algorithm. A large class of these problems can be expressed as ML parameter estimation where the output features are ML parameter estimates. PDF projection for problems of this type has been discussed in Section 2.3.5. We now discuss a concrete example in which the data model follows a Gaussian distribution with non-linear parameter dependence.



Subsections

Baggenstoss 2017-05-19