title: Bayesian learning and predictability in a stochastic nonlinear dynamical model.
abstract: Bayesian inference methods are applied within a Bayesian hierarchical modeling 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. The approach is applied to a case study at Ocean Station Papa, using particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations.
authors.affiliation: CSIRO Computational and Simulation Science, Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia.
authors.forename: John
authors.initials: J
authors.lastname: Parslow
authors.forename: Noel
authors.initials: N
authors.lastname: Cressie
authors.forename: Edward P
authors.initials: EP
authors.lastname: Campbell
authors.forename: Emlyn
authors.initials: E
authors.lastname: Jones
authors.forename: Lawrence
authors.initials: L
authors.lastname: Murray
mesh_terms: D001499:Bayes Theorem; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods / Q000706:statistics & numerical data; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010140:Pacific Ocean; D010933:Plankton / Q000502:physiology; D011157:Population Dynamics; D013269:Stochastic Processes; D013997:Time Factors