In recent years, Bayesian statistics has been attracting attention not only in economics and finance, but also in various fields such as business administration, medicine, psychology, and marketing.
In this lecture, we will explain the differences between Bayesian statistics and non-Bayesian statistics, Bayes' theorem which is the basis of Bayesian statistics, Bayesian updating from prior to posterior probability, Markov chain Monte Carlo method which is a numerical analysis method, model selection in Bayesian statistics, application of Bayesian estimation to normal distribution models and regression analysis models, and hierarchical Bayesian models which handle individual differences.
In the lecture, we will include many exercises using Python.