Limit theorems for chains with unbounded variable length memory which satisfy Cramer condition

Publications | May 15, 2020

Artem Logachov, A. Mogulskii and Anatoly Yambartsev

Here we obtain the exact asymptotics for large and moderate deviations, strong law of large numbers and central limit theorem for chains with unbounded variable length memory.

Neuromat Online Seminars

Events | Apr 20, 2020

The Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) will hold regular online team-wide seminars to present and discuss research progress. Guests and team members will be included in the program.

Neuromathematics research center holds six webinars on mathematics and neurobiology

News | Apr 20, 2020

This week, "Agência FAPESP" website featured an article about the RIDC NeuroMat series of online seminars on mathematics and neurobiology, NeuroMat/NeuroMod webinars 2020 - mathematics and neurobiology intertwined.

The webinars are part of a partnership with theInstitute for Modeling in Neuroscience and Cognition of Université Côte d'Azur (NeuroMod), France. The virtual meetings will take place every two weeks, from April 16 to June 25, 2020, via Google Meet at meet.google.com/xdz-rqup-dze.

NeuroMat/NeuroMod webinars 2020

Events, Newsletter | Apr 07, 2020

Amidst difficulties associated with the COVID-19 pandemics worldwide, in a context in which universities are closed and scientific missions are prohibited, we must do our best to keep scientific life alive. The series of webinars "Mathematics and Neurobiology Intertwined", presented on this page, bears the distinctive feature of bonding efforts and teams to move science forward.

A note on perfect simulation for Exponential Random Graph Models

Publications | Apr 03, 2020

Andressa Cerqueira, Aurélien Garivier and Florencia Leonardi

In this paper, we propose a perfect simulation algorithm for the Exponential Random Graph Model, based on the Coupling from the past method of Propp and Wilson (1996). We use a Glauber dynamics to construct the Markov Chain and we prove the monotonicity of the ERGM for a subset of the parametric space. We also obtain an upper bound on the running time of the algorithm that depends on the mixing time of the Markov chain.

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