Summary

We began our Bayesian journey with a very brief discussion about statistical modeling, probability theory and an introduction of the Bayes' theorem. We then use the coin-tossing problem as an excuse to introduce basic aspects of Bayesian modeling and data analysis. We used this classic example to convey some of the most important ideas of Bayesian statistics such as using probability distributions to build models and represent uncertainties. We tried to demystify the use of priors and put them on an equal footing with other elements that we must decide when doing data analysis, such as other parts of the model like the likelihood, or even more meta questions like why are we trying to solve a particular problem in the first place. We ended the chapter discussing the interpretation and communication of the results of a Bayesian analysis. In this chapter we have briefly summarized the main aspects of doing Bayesian data analysis. Throughout the rest of the book we will revisit these ideas to really absorb them and use them as the scaffold of more advanced concepts. In the next chapter we will focus on computational techniques to build and analyze more complex models and we will introduce PyMC3 a Python library that we will use to implement and analyze all our Bayesian models.