  Information-theoretic quantities, such as entropy, are used to quantify the
amount of information a given variable provides. Entropies can be used together
to compute the mutual information, which quantifies the amount of information
two variables share. However, accurately estimating these quantities from data
is extremely challenging. We have developed a set of computational techniques
that allow one to accurately compute marginal and joint entropies. These
algorithms are probabilistic in nature and thus provide information on the
uncertainty in our estimates, which enable us to establish statistical
significance of our findings. We demonstrate these methods by identifying
relations between cloud data from the International Satellite Cloud Climatology
Project (ISCCP) and data from other sources, such as equatorial pacific sea
surface temperatures (SST).
