High-dimensional MCMC algorithms for Bayesian models

On September 21st, 2021 at 4:00 pm, within the context of the DataCloud activities, Proff. Alessandra Guglielmi and Mario Beraha of DMAT Department – Politecnico di Milano, will hold the online seminar titled “High-dimensional MCMC algorithms for Bayesian models“.

In this talk we will introduce the Bayesian approach to inferential statistics and sketch Markov chain Monte Carlo (MCMC) algorithms, that are a class of simulation methods typically used to approximate integrals with respect to probability measures involved in posterior inferences. In general, Bayesian models consider a very large number of parameters, and hence MCMC algorithms with a highly dimensional parameter space need to be designed, with extremely large associated computational cost. As an illlustration we will consider some applications where the goal is (model-based) clustering the data. We use Bayesian mixture models and we will see that, in case of high-dimensional data, the corresponding MCMC algorithms are computationally very demanding.at 

Date(s) – 21/09/2021
Time 04:00 pm
Contacts: Elisabetta Di Nitto
Place: Politecnico di Milano – on-line event