Dr. Alberto G Busetto (UCSB) 

Title: Near-optimal Bayesian design of experiments for model selection of nonlinear dynamical systems"

Abstract: The identification of dynamic processes is both statistically and computationally challenging. This talk introduces formal guarantees of near-optimal Bayesian design of experiments aimed selecting nonlinear dynamical systems from data. We prove by reduction to graphical modeling and maximal coverage approximation that the joint selection of the most informative time points and components of the state space can be performed in polynomial-time and near-optimally, and with the best constant approximation factor unless P=NP. We further discuss the case of selecting active interventions, and show that these same bounds apply under certain sufficient conditions, for instance when it is possible to restart the system, or perform experiments in parallel. We conclude by reporting our results in applications of concrete biomedical interest, such as phosphoproteomics and personalized treatment.