Bayesian Statistics by Coursera

This course will explain Bayesian statistics. In Bayesian statistics, one's inferences regarding parameters or hypotheses can be updated as evidence accumulates. Bayes' rule will be used to convert prior probabilities into posterior probabilities. You will also learn the theory and perspectives behind the Bayesian paradigm. Bayesian methods will be applied to practical problems to demonstrate end-to-end Bayesian analyses. This includes building models, framing questions, eliciting prior probabilities, and finally implementing the final posterior distribution in R (free statistical program). The course will also introduce credible regions, Bayesian comparisons between means and proportions and Bayesian inference using multiple model, Bayesian regression, and Bayesian prediction.

This course assumes that learners have the same background knowledge as those in the three previous courses in this specialization, "Introduction to Probability & Data," "Inferential Statistic," and "Linear Regression & Modeling."