Bayesian Statistics with Applications in Econometrics, Finance and Machine Learning using Python


Preface

Welcome to the fascinating world of Bayesian statistics. Bayesian statistics is a powerful framework for reasoning about uncertainty. Unlike the traditional frequentist approach, which treats model parameters as fixed, unknown constants, the Bayesian paradigm views parameters as random variables. This means we can have beliefs about them, represented by probability distributions, and we can update these beliefs as we gather evidence. 
This single, powerful shift in perspective provides a unified and intuitive framework for statistical inference, decision-making, and prediction.
Throughout this text, we will build our understanding from the ground up. We'll start with the foundational Bayes' theorem, contrast the Bayesian and frequentist philosophies, and move to advanced computational techniques like Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). We will emphasize hands-on implementation using the Python library PyMC, applying these methods to real-world problems in econometrics, finance, and machine learning.
By the end of this course, you will not only understand the "what" and "why" of Bayesian statistics but also the "how," equipping you to build, fit, and critique sophisticated statistical models.