Probability and Bayesian statistics

Bayesian statistics is an approach to statistical inference, derived from Bayes' theorem, a mathematical equation built off simple probability axioms. It allows an analyst to calculate any conditional probability of interest. A conditional probability is simply the probability of event A given that event B has occurred.

Therefore, in probability terms, the data events have already occurred and have been collected (since we know the probability). By using Bayes' theorem, we can then calculate the probability of various things of interest, given or conditional upon, this already observed data.

Bayesian modeling is beyond the scope of this book, but again the underlying data models are well handled using pandas and then actually analyzed using libraries such as PyMC.