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Introduction to Bayesian Computing


Mira A.

Docente titolare del corso


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”

Modalità di insegnamento

In presenza

Impostazione pedagogico-didattica

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

Modalità d’esame

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

Offerta formativa