Introduction to Bayesian Computing
The students will be able to estimate a complex Bayesian model and to provide estimates uncertainty quantifications.
Students need to have passed the exam "Introduction to Data Science"
- Markov chains
- Monte Carlo simulation
- Importance sampling
- Markov chain Monte Carlo (MCMC) methods
- Adaptive MCMC
- MCMC convergence diagnostics
- Approximate Bayesian Computation
Weekly lectures will be complemented with tutorials and practicals (with R statistical software)
A final exam worth 100% of the final grade.
Students are requested to bring to class their own laptop with the R statistical software installed.
The material needed for this course will be provided during the course itself in the form of slides, papers and reference books.
Examples of reference books are:
- Bayesian Essentials with R, Jean-Michel Marin, Christian P. Robert
- Introducing Monte Carlo Methods with R, Christian Robert, George Casella
- Markov Chain Monte Carlo in Practice, W.R. Gilks, S. Richardson, David Spiegelhalter
- Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition, Dani Gamerman and Hedibert F. Lopes