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Article
Bayesian learning and predictability in a stochastic nonlinear dynamical model
Faculty of Engineering and Information Sciences - Papers: Part A
  • John Parslow, Csiro, Marine And Atmospheric Research
  • Noel Cressie, University of Wollongong
  • Edward P Campbell, Csiro, Mathematics, Informatics & Statistics
  • Emlyn Jones, Csiro, Marine And Atmospheric Research
  • Lawrence Murray, Csiro, Mathematics, Informatics & Statistics
RIS ID
74591
Publication Date
1-1-2013
Publication Details

Parslow, J., Cressie, N., Campbell, E. P., Jones, E. & Murray, L. (2013). Bayesian learning and predictability in a stochastic nonlinear dynamical model. Ecological Applications, 23 (4), 679-698.

Abstract
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. 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.
Citation Information
John Parslow, Noel Cressie, Edward P Campbell, Emlyn Jones, et al.. "Bayesian learning and predictability in a stochastic nonlinear dynamical model" (2013)
Available at: http://works.bepress.com/noel_cressie/298/