Introduction to Bayesian Computing
People
Description
Prerequisites: students need to have had an introduction to the Bayesian paradigm: prior, likelihood and posterior distribution. Topics that will be covered: Advanced Bayesian inference, Markov chains, Monte Carlo simulation, Importance sampling, Markov chain Monte Carlo (MCMC) methods, Adaptive MCMC, MCMC convergence diagnostics, Approximate Bayesian Computation.
Objectives
The students will be able to estimate a complex Bayesian model and to provide corresponding uncertainty quantifications. COURSE PREREQUISITES Students need to have passed the exam “Introduction to Data Science”
Teaching mode
In presence
Learning methods
Weekly lectures will be complemented with tutorials and practicals (with R statistical software)
Examination information
A final exam worth 100% of the final grade.
Education
- Master of Science in Computational Science, Lecture, Elective, 1st year
- Master of Science in Computational Science, Lecture, Elective, 2nd year
Prerequisite
- Introduction to Data Science, Wit E. J., Ceoldo G., SA 2021-2022
