Speaker
Giacomo Gori
(ICTP)
Description
Parameter estimation is a central issue in system modeling,
the typical setting is to start with a given set of
measurements and extract the parameters of
a model supposed to describe the system under scrutiny. The
recent availability of large datasets coming from the complex
system has made even more pressing the quest for efficient
models, and the related parameter extraction techniques.
We study Ising chains with arbitrary multispin finite-range
couplings, providing an explicit solution of the associated
inverse Ising problem, i.e. the problem of inferring the values
of the coupling constants from the correlation functions. As
an application, we reconstruct the couplings of chain Ising
Hamiltonians having exponential or power-law two-spin plus
three- or four-spin couplings. The generalization of the
method to ladders and to Ising systems where a mean-field
interaction is added to general finite-range couplings is also
discussed.
