Day 1
Session 1: Introduction to Bayesian Statistics

• What is Bayesian statistics, and why use it?
• Differences between Bayesian and frequentist approaches
• Hands-on lab: Exploring Bayes’ theorem with simple examples

Session 2: Prior and Posterior Distributions

• Understanding prior beliefs and how they influence analysis
• Combining priors with likelihood to derive posterior distributions
• Hands-on lab: Defining and using priors in a Bayesian model

Session 3: Bayesian Inference Basics

• Using data to update probabilities and beliefs
• Examples of inference in real-world scenarios
• Hands-on lab: Performing Bayesian inference on a dataset

Day 2
Session 1: Markov Chain Monte Carlo (MCMC) Methods

• Introduction to MCMC and its role in Bayesian analysis
• Common algorithms: Metropolis-Hastings, Gibbs sampling
• Hands-on lab: Implementing MCMC in Python or R

Session 2: Bayesian Regression Models

• Building and interpreting Bayesian linear and logistic regression models
• Comparing Bayesian regression with frequentist methods
• Hands-on lab: Applying Bayesian regression to real-world data

Session 3: Model Evaluation and Diagnostics

• Assessing model convergence and performance
• Tools for diagnosing and improving Bayesian models
• Hands-on lab: Evaluating and refining Bayesian models

Day 3
Session 1: Advanced Bayesian Applications

• Hierarchical Bayesian models for complex data structures
• Decision-making under uncertainty using Bayesian methods
• Hands-on lab: Building a hierarchical Bayesian model

Session 2: Real-World Case Study: Bayesian Analysis in Practice

• Applying Bayesian methods to solve a real-world problem
• Group activity: Collaborating on a Bayesian analysis project

Session 3: Communicating Bayesian Results

• Best practices for visualizing and explaining Bayesian insights
• Creating compelling narratives for stakeholders
• Group discussion: Sharing insights and lessons learned