In this chapter, we undertook a whirlwind tour of one of the hottest trends in statistics and data analysis in the past few years—the Bayesian approach to statistical inference. We covered a lot of ground here.

We examined what the Bayesian approach to statistics entails and discussed the various factors as to why the Bayesian view is a compelling one—facts over belief. We explained the key statistical distributions and showed how we can use the various statistical packages to generate and plot them in matplotlib.

We tackled a rather difficult topic without too much oversimplification and demonstrated how we can use the PyMC package and Monte Carlo simulation methods to showcase the power of Bayesian statistics to formulate models, do trend analysis, and make inferences on a real-world dataset (Facebook user posts). In the next chapter, we will discuss the pandas library architecture.