High-dimensional MCMC algorithms for Bayesian models

On September 21st, 2021 at 04:00 pm, within the context of the DataCloud activities, Prof. Alessandra Guglielmi and Prof. 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, which 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 illustration 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.

 

The event will be held online in Microsoft Teams


Data / Ora
Date(s) - 21/09/2021
16:00 -17:00

Contatti
Prof.ssa Elisabetta Di Nitto

Luogo
This event will be held online.