Clearly, designing or estimating high-dimensional
generative models for
competely unknown natural processes or man-made signals
in natural noise is daunting.
But high-dimensions does not deter anyone from
designing signal processing algorithms.
We propose that signal processing and generative model
design is one and the same.
The signal processing chain is designed
to remove nuisance information
or concentrate signal energy based on
a theoretical or intuitive understanding.
This process of concentrating information
and removing noise and nuisance information is in general
In MaxEnt PDF projection, which we will describe shortly,
the signal processing chain, seen as a dimension-reducing
, together with the
specified or estimated feature PDF
uniquely defines a complete generative model
for which we can compute the likelihood function
and generate unlimited random samples.
The signal processing can be comprised
of many stages and can even be iterative
if posed as a maximum likelihood estimator.