  This paper is directed towards the problem of learning nonlinear ARX models
based on system input--output data. In particular, our interest is in learning
a conditional distribution of the current output based on a finite window of
past inputs and outputs. To achieve this, we consider the use of so-called
energy-based models, which have been developed in allied fields for learning
unknown distributions based on data. This energy-based model relies on a
general function to describe the distribution, and here we consider a deep
neural network for this purpose. The primary benefit of this approach is that
it is capable of learning both simple and highly complex noise models, which we
demonstrate on simulated and experimental data.
