Previously, most Bayesian analyses were restricted to simple, limited applications. With the development of Markov Chain Monte Carlo methods, Bayesian inference has become an important applied technique and has been able to handle complex problems.
Some problems are now easier to compute with Bayesian methods than with frequentist methods.
This course will first explain the basics of Bayesian inference and Markov chain Monte Carlo methods. From there, this course will show how to compute and make inferences on complex data problems using these methods. There will be four homework problem sets. Each problem set will include methodological and computational exercises. Each student will also work on an individual final project.