Seminar on 31 May 2018  at DFA  Aula F  h. 15.30

 

Probabilistic modeling with information filtering networks

 

Prof. Tomaso Aste 

 

University College London

 

Abstract 

 

We are witnessing interesting times rich of information, readily available for us all. Using, understanding and filtering such information has become a major activity across science, industry and society at large.

I will show how information filtering networks [1-3] built from similarity measures, both linear and non-linear, can be used to process information while it is generated reducing complexity and dimensionality while keeping the integrity of the dataset. I’ll describe  how a predictive probabilistic modeling can be associate to such networks. Through exemplification with applications to financial systems I will demonstrate how reliable, predictive and useful these models are [4]. 

 

[1] Tumminello, Michele, Tomaso Aste, Tiziana Di Matteo, and Rosario N. Mantegna. "A tool for filtering information in complex systems." Proceedings of the National Academy of Sciences of the United States of America 102, no. 30 (2005): 10421-10426.

[2]Aste, Tomaso, Tiziana Di Matteo, and S. T. Hyde. "Complex networks on hyperbolic surfaces." Physica A: Statistical Mechanics and its Applications 346, no. 1-2 (2005): 20-26.

[3] Massara, Guido Previde, Tiziana Di Matteo, and Tomaso Aste. "Network filtering for big data: triangulated maximally filtered graph." Journal of complex Networks 5, no. 2 (2016): 161-178.

[3] Massara, Guido Previde, Tiziana Di Matteo, and Tomaso Aste. "Network filtering for big data: triangulated maximally filtered graph." Journal of complex Networks 5, no. 2 (2016): 161-178.

[4] Aste, Tomaso, W. Shaw, and Tiziana Di Matteo. "Correlation structure and dynamics in volatile markets." New Journal of Physics 12, no. 8 (2010): 085009.