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Bayesian learning and predictability in a stochastic nonlinear dynamical model

Journal Article


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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.

Authors


  •   Parslow, John (external author)
  •   Cressie, Noel A.
  •   Campbell, Edward (external author)
  •   Jones, Emlyn (external author)
  •   Murray, Lawrence (external author)

Publication Date


  • 2013

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84878935744

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2171&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1162

Number Of Pages


  • 19

Start Page


  • 679

End Page


  • 698

Volume


  • 23

Issue


  • 4

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.

Authors


  •   Parslow, John (external author)
  •   Cressie, Noel A.
  •   Campbell, Edward (external author)
  •   Jones, Emlyn (external author)
  •   Murray, Lawrence (external author)

Publication Date


  • 2013

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84878935744

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2171&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1162

Number Of Pages


  • 19

Start Page


  • 679

End Page


  • 698

Volume


  • 23

Issue


  • 4