An important test of a mathematical model is how well it describes a given phenomenon, which is often observed only indirectly and with error.  This talk will provide an introduction to the statistical problem of inference for nonlinear dynamical systems. We will take a Bayesian perspective, constructing hierarchical models that incorporate prior knowledge and impose required constraints on model components.