Introduction to Bayesian Computing
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, Markov chain Monte Carlo (MCMC) methods, Adaptive MCMC, MCMC convergence diagnostics, Approximate Bayesian Computation.
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”
Weekly lectures will be complemented with tutorials and practicals (with R / Python statistical software)
A final exam worth 100% of the final grade. Class participation will also be considered.