  Given a default distribution $P$ and a set of test data
$x^M=\{x_1,x_2,\ldots,x_M\}$ this paper seeks to answer the question if it was
likely that $x^M$ was generated by $P$. For discrete distributions, the
definitive answer is in principle given by Kolmogorov-Martin-L\"{o}f
randomness. In this paper we seek to generalize this to continuous
distributions. We consider a set of statistics $T_1(x^M),T_2(x^M),\ldots$. To
each statistic we associate its maximum entropy distribution and with this a
universal source coder. The maximum entropy distributions are subsequently
combined to give a total codelength, which is compared with $-\log P(x^M)$. We
show that this approach satisfied a number of theoretical properties.
  For real world data $P$ usually is unknown. We transform data into a standard
distribution in the latent space using a bidirectional generate network and use
maximum entropy coding there. We compare the resulting method to other methods
that also used generative neural networks to detect anomalies. In most cases,
our results show better performance.
