Summary

This chapter provides a comprehensive introduction to Bayesian statistics, beginning with an exploration of the fundamental Bayes’ theorem. We delved into its components, starting with understanding the generative model, which helps us simulate data and examine how changes in parameters affect the data generation process.

We then focused on understanding the prior distribution, an essential part of Bayesian statistics that represents our prior knowledge about an uncertain parameter. This was followed by an introduction to the likelihood function, a statistical function that determines how likely it is for a set of observations to occur given specific parameter values.

Next, we introduced the concept of the posterior model. This combines our prior distribution and likelihood to give a new probability distribution that represents updated beliefs after having seen the data. We also explored more complex models, such as the normal-normal model, wherein both the likelihood...