Applied Bayesian Analysis with R

Description

This course provides a comprehensive, hands-on approach to Bayesian statistics, focusing on fundamental concepts and practical applications using R. Designed for beginners and those with some statistical background, this course will guide you through the core principles of Bayesian analysis, allowing you to understand and apply these methods to real-world data.

Course Structure

Lecture 1: Why Bayes? Introduction and Welcome
We start with a fundamental question: Why Bayesian statistics? This lecture introduces the advantages of Bayesian thinking, contrasting it with frequentist methods to highlight how Bayesian analysis provides a flexible, intuitive approach to data. This session sets the stage for understanding the Bayesian perspective and what you can expect to gain from this course.

Lecture 2: R Setup for Bayesian Statistics
In this session, we’ll set up R for Bayesian analysis, covering essential packages and libraries, and walk through basic commands for data manipulation and visualization. By the end, you'll be equipped with the tools needed to dive into Bayesian modeling.

Lecture 3: The Bayesian Trinity: Priors, Likelihood, and Posteriors
Here, we explore the three central components of Bayesian analysis: priors, likelihood, and posteriors. We’ll discuss how these elements interact to shape Bayesian inference and will use R to visualize how prior beliefs combine with data to form posterior distributions.

Lecture 4: Bayesian Regression in R
This lecture delves into Bayesian regression, covering linear models in a Bayesian framework. You'll learn how to specify priors, compute posterior distributions, and interpret results, building on classical regression knowledge to gain a Bayesian perspective.

Lecture 5: Logistic Regression and Predictions
Expanding on regression techniques, this session introduces Bayesian logistic regression, ideal for binary outcomes and classification. You’ll learn to make probabilistic predictions and understand uncertainty, essential for interpreting results in Bayesian analysis.

Lecture 6: Diagnostics and Visualization
Diagnostics are critical for ensuring model reliability. This lecture covers methods for evaluating model fit, assessing convergence, and visualizing posterior distributions. We’ll use R’s plotting tools to gain insight into model behavior, helping you detect and address potential issues.

Lecture 7: Practical Tips and Conclusions
In our final lecture, we’ll discuss practical tips for successful Bayesian analysis, including choosing priors, understanding model limitations, and interpreting results. We’ll review key takeaways and best practices, equipping you with a well-rounded foundation to apply Bayesian methods confidently.

This course is designed to be interactive, providing hands-on exercises to reinforce concepts and develop practical skills in Bayesian statistics using R. By the end, you'll have the tools and knowledge to apply Bayesian thinking to real-world data analysis challenges confidently. Welcome, and let’s begin our Bayesian journey!

Who this course is for:

• Researchers and analysts seeking to learn applied statistical modeling