  We consider the following problem: we have a large dataset of normal data
available. We are now given a new, possibly quite small, set of data, and we
are to decide if these are normal data, or if they are indicating a new
phenomenon. This is a novelty detection or out-of-distribution detection
problem. An example is in medicine, where the normal data is for people with no
known disease, and the new dataset people with symptoms. Other examples could
be in security. We solve this problem by training a bidirectional generative
adversarial network (BiGAN) on the normal data and using a Gaussian graphical
model to model the output. We then use universal source coding, or minimum
description length (MDL) on the output to decide if it is a new distribution,
in an implementation of Kolmogorov and Martin-L\"{o}f randomness. We apply the
methodology to both MNIST data and a real-world electrocardiogram (ECG) dataset
of healthy and patients with Kawasaki disease, and show better performance in
terms of the ROC curve than similar methods.
