Introduction to Bayesian Computing
The students will be able to estimate a complex Bayesian model and to provide estimates uncertainty quantifications.
- Markov chains
- Monte Carlo simulation
- Importance sampling
- Markov chain Monte Carlo (MCMC) methods
- Adaptive MCMC
- MCMC convergence diagnostics
- Approximate Bayesian Computation
There will be theoretical and applied (with R statistical software) frontal and online lectures compliant with COVID-19 guidelines.
A project that will involve the analysis of data using the computational methodologies learned during the course will count for 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.
- Master of Science in Computational Science, Core course, Lecture, 1st year