A. Sarkar

Semi-analytical hierarchical Bayesian inference of nonlinear model structure in stochastic dynamics: Applied to compartmental models of infectious diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented, and it is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.