Maximum Likelihood
Overview
Neural Networks can be used to approximate any function, by the
Universal Approxmiation Theorem.
This means in particular that a neural network can be used to approximate a
Maximum Likelihood function.
That is given a data sample, one can train a neural network using a loss function that is the negative of the
log likelihood function.
Examples
The following examples give concrete examples of fitting a neural network using maximum likelihood.
