A good way to explain a dataset is to apply a probabilistic model to it. Finding an adequate model can be a job on its own. Once a model is chosen, it is necessary to compare it to the data. This is what statistical estimation is about. In this recipe, we apply the maximum likelihood method on a dataset of survival times after heart transplant (1967-1974 study).
As usual in this chapter, a background in probability theory and real analysis is recommended. In addition, you need the
statsmodels package to retrieve the test dataset. It should be included in Anaconda, but you can always install it with the
conda install statsmodels command.