MI may be exactly obtained only if the distribution of variables is known. In most cases, the underlying distribution of data is not known, and the distribution, and thus the MI, must be estimated approximately. A number of methods to estimate MI have been developed, including approaches that use cumulant expansions , kernel density estimators , and adaptive binning . One successful algorithm to estimate MI, which we particularly focus on, uses a k-nearest neighbor approach to estimate the densities of the data within the distribution . The k-nearest neighbor approach to estimating MI has been utilized in a number of interesting biophysics applications . The advantages of the k-nearest neighbors approach include data efficiency (it works well for smaller data sets compared to other estimators), adaptive (the algorithm automatically uses higher resolution where the data is dense), and minimal bias.