Fitting a probability distribution to data with the maximum likelihood method
A good way to explain a dataset is to apply a probabilistic model to it. Finding an adequate model can be a job in 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).
Getting ready
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. For more information on statsmodels, refer to http://statsmodels.sourceforge.net. On Anaconda, you can install statsmodel with the conda install statsmodels command.
How to do it...
statsmodels is a Python package for conducting statistical data analyses. It also contains real-world datasets that we can use when experimenting with new methods. Here, we load the heart dataset...
