Identifying risk spillovers in financial markets is of great importance for assessing systemic
risk and portfolio management. Granger causality in tail (or in risk) tests whether past …

We consider the problem of inferring a causality structure from multiple binary time series by
using the kinetic Ising model in datasets where a fraction of observations is missing. Inspired …

We propose a method to infer lead-lag networks of traders from the observation of their trade
record as well as to reconstruct their state of supply and demand when they do not trade …

We propose an efficient algorithm to solve inverse problems in the presence of binary
clustered datasets. We consider the paradigmatic Hopfield model in a teacher student …

A common issue when analyzing real-world complex systems is that the interactions
between their elements often change over time. Here we propose a new modeling approach …

Inferring dynamics from time series is an important objective in data analysis. In particular, it
is challenging to infer stochastic dynamics given incomplete data. We propose an …

Binary random variables are the building blocks used to describe a large variety of systems,
from magnetic spins to financial time series and neuron activity. In statistical physics the …

The usual setting for learning the structure and parameters of a graphical model assumes
the availability of independent samples produced from the corresponding multivariate …

There are many methods proposed for inferring parameters of the Ising model from given
data, that is a set of configurations generated according to the model itself. However little …

Understanding the relation between cortical neuronal network structure and neuronal
activity is a fundamental unresolved question in neuroscience, with implications to our …