Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model

Understand

Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting.

• We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model.
• A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes.
• This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters.