Bayesian statistics provide an alternative way of interpreting probability and an alternative way of dealing with statistics, compared to the more common frequentist interpretation. In this course, Bayesian Statistics and Probabilistic Programming in R, you'll gain the ability to implement and interpret Bayesian models in R using the brms package. First, you'll explore the Bayesian theorem and its mathematical underpinnings. Next, you'll discover how to select prior probabilities and update them with experimental data to produce a model which explains the experimental data and can be used for further inference. Finally, you'll learn how to implement those models in R, using the brms package. When you're finished with this course, you'll have the skills and knowledge of Bayesian statistics needed to perform statistical analysis on a wide variety of real-world data sets and problems.