Introduction to Bayesian Data Analysis and Modeling with R

Traditional statistical methods often fall short when you need to incorporate prior knowledge or update your beliefs as new data arrives. Bayesian analysis offers a powerful alternative, allowing you to estimate likely event outcomes with greater flexibility and precision. This course guides you from the fundamental concepts of probability to building and interpreting your first Bayesian models in R. You will transition from understanding basic statistical definitions to writing clean R scripts that calculate priors, likelihoods, and posteriors, giving you a robust framework for real-world data decision-making. What you'll learn: - Understand the core differences between frequentist and Bayesian approaches to statistical inference - Apply Bayes' theorem to calculate prior, likelihood, and posterior probabilities - Write R scripts to set up, run, and analyze single-parameter models and conjugate mixtures - Utilize RStudio and modern R programming workflows to manage data and structure computational analyses - Interpret posterior distributions to make informed, probabilistic predictions about event outcomes - Implement best practices for reproducible research and modern code organization in R The course begins with foundational probability concepts and an introduction to R scripting, ensuring you are comfortable with the programming environment. From there, you will progress through single-parameter models, conjugate mixtures, and practical estimation techniques using written explanations and step-by-step code walkthroughs. This course is designed for beginners, data analysts, and researchers who want to learn Bayesian statistics. No prior experience with R or advanced mathematics is required, as we build all concepts from the ground up. Start your journey into Bayesian data analysis and unlock deeper insights from your data today.