EXAMPLES OF FLEXIBLE BAYESIAN REGRESSION AND CLASSIFICATION MODELS BASED ON 
NEURAL NETWORKS AND GAUSSIAN PROCESSES

Neural networks and Gaussian processes can be used to to predict a
target value (or values) from a set of input values.  When the target
is real-valued, this is known as "regression".  When the target takes
on values from a finite set, it is known as "classification".
Examples of both sorts are discussed here.

The data and command files for these examples are in the "ex-netgp"
directory.