1. Final remarks
Congratulations on completing the course! Let's do a quick recap!
2. What you know
In Chapter 1, you learned about the difference between Bayesian and frequentist approaches. You also learned about probability theory, probability distributions, and how a Bayesian model updates its beliefs.
In Chapter 2, you learned about grid approximation, prior distributions, and how to effectively report Bayesian results.
In Chapter 3, you were introduced to A/B testing, applying Bayesian analysis to decision-making, and regression and forecasting.
Finally, in Chapter 4, you learned how MCMC methods work, used the pymc3 package to fit and interpret regression models, and concluded with a case study.
3. More Bayes
But the journey doesn't end here!
The Bayesian framework allows us to combine multiple regression equations into what's called hierarchical models.
There are also Bayesian versions of virtually all common statistical models, such as logistic or Poisson regression.
Next, many popular machine learning techniques rely on Bayesian inference. There are even Bayesian neural networks!
For further information, I encourage you to check out the pymc3 documentation and Allen Downey's Think Bayes, which is a great introductory read. It is available for free on the author's website.
4. Congratulations and good luck!
Once again, congratulations! I hope the knowledge and skills you've gained will help you generate insights from your data!