Speaker
Dr
Matteo Marsili
(Abdus Salam ICTP)
Description
Advanced inference techniques allow one to reconstruct the
pattern of interaction from high dimensional data sets. We
focus here on the statistical properties of inferred models
and argue that inference procedures are likely to yield
models which are close to a phase transition. On one side,
we show that the reparameterization invariant metrics in
the space of probability distributions of these models (the
Fisher Information) is directly related to the model's
susceptibility. As a result, distinguishable models tend to
accumulate close to critical points, where the susceptibility
diverges in infinite systems. On the other, this region is the
one where the estimate of inferred parameters is most
stable. In order to illustrate these points, we discuss
inference of interacting point processes with application to
financial data and show that sensible choices of observation
time-scales naturally yield models which are close to
criticality.
