Course overview
The course aims to equip students with the modern tools of a statistician for intensive computing in the Bayesian or variational approaches. Students will learn to implement and tune a variety of MCMC samplers and algorithms for inference in complex problems.
- Foundations of Inference and Simulation
- Markov Chain Monte Carlo
- Further Topics
Course learning outcomes
- Describe and implement statistical algorithms to perform inference in a complex statistical problem
- Interpret output and critically appraise algorithm performance
- Choose and justify suitable approaches to model problems
Degree list
The following degrees include this course