Stefano crotti correggio biography

When dealing with stochastic processes on graphs, two elements can render the estimation of observables computationally hard: first, the presence of reweighting terms that bias the probability measure toward rare events and, second, the possibility for variables to return to a previously visited state.

While in the past, several approximation methods have successfully dealt with either of these two factors, our work aims at addressing both simultaneously.

This talk will introduce the Matrix Product Belief Propagation approximation, which can be applied to Markov processes biased by arbitrary reweighting factors that concentrate most of the probability mass on a small subspace of trajectories.

Our approach builds on a version of the dynamic cavity method where distributions of pairs of trajectories are parametrized by matrix product states, a well-known tool in the field of many-body quantum systems. Two applications will be mentioned: Bayesian inference on epidemic models and the ana