  Mutual information is an important measure of the dependence among variables.
It has become widely used in statistics, machine learning, biology, etc.
However, the standard techniques for estimating it often perform poorly in
higher dimensions or with noisy variables. Here we significantly improve one of
the standard methods for calculating mutual information by combining it with a
modified Monte Carlo Tree Search. We present results which show that our method
gives accurate results where the standard methods fail. We also describe the
software implementation of our method and give details on the
publicly-available code.
