- Bayesian Statistics

Catalog description

Statistics is the science of turning data into informed conclusions. The Bayesian approach to statistics uses probability theory to model both the uncertainty inherent in sampling from a population, and also our own degree of uncertainty in the answers we expect to get. That is, we begin with a prior distribution representing what we know or believe about a population, then we integrate new data that arrives into this distribution, resulting in a posterior distribution representing our updated beliefs. With this framework, an analysis can be informed by all past analyses on the topic (even if they analyzed other datasets), and we can construct an iterative approach that converges to the truth. This course introduces the Bayesian perspective, Bayesian approaches to statistical models you have seen in other classes, and new models that are only possible in the Bayesian approach. Course topics are demonstrated via real-world applications.