Dear all,

We are glad to invite you to the *seminar* that will take place on the *26th of October, at 14.30*, in presence in Room *Aula Seminari DiSMeQ 4026, building U7* Civitas, 4th floor, *University of Milano-Bicocca*, Via Bicocca degli Arcimboldi 8, 20126 Milano.

*Catherine Matias* http://cmatias.perso.math.cnrs.fr/from *Centre National de la Recherche Scientifique (CNRS), Sorbonne University, Paris* will present a seminar on “*Properties of the stochastic approximation EM algorithm with the mini-batch sampling*” (see abstract below).

The seminar is also available online at the following link:

https://unimib.webex.com/unimib-it/j.php?MTID=m414dda85e475f906edae49b2a8eee... https://unimib.webex.com/unimib-it/j.php?MTID=m414dda85e475f906edae49b2a8eee193

Passcode: NmEQKpfG535 (66375734 from phones)

You are invited to forward the event to your students, PhDs and colleagues who may be interested in the Seminar.

Kind Regards,

Fulvia Pennoni

On behalf of the Department of Statistics and Quantitative Methods

/--------------/

*Speaker*: Catherine Matias from Centre National de la Recherche Scientifique (CNRS), Sorbonne University, Paris

*Title*: /Properties of the stochastic approximation EM algorithm with the mini-batch sampling/

*Abstract*: To deal with very large datasets a mini-batch version of the Monte Carlo Markov Chain Stochastic Approximation Expectation–Maximization algorithm for general latent variable models is proposed. For exponential models the algo rithm is shown to be convergent under classical conditions as the number of iterations increases. Numerical experiments illustrate the performance of the mini-batch algorithm in various models. In particular, we highlight that mini-batch sampling results in an important speed-up of the convergence of the sequence of estimators generated by the algorithm. Moreover, insights on the effect of the mini-batch size on the limit distribution are presented. Finally, we illustrate how to use mini-batch sampling in practice to improve results when a constraint on the computing time is given.