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Introduction to Bayesian Computing


Mira A.

Course director

Ravenda F.



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”

Teaching mode

In presence

Learning methods

Weekly lectures will be complemented with tutorials and practicals (with R / Python statistical software)

Examination information

 A final exam worth 100% of the final grade. Class participation will also be considered.