A Bayesian approach to the ecosystem inverse problem

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A sequential Monte Carlo approach for marine ecological prediction (2006, methodology): “…approaches to include model errors, stochastic forcing and nonGaussian observations, Markov chain Monte Carlo (MCMC) methods have also been used, both for ecological parameter estimation (Harmon and Challenor, 1997) as well as for joint state and parameter estimation (Dowd and Meyer, 2003).”
Review of recent carrying capacity models for bivalve culture and recommendations for research and management (2006, methodology): “A promising method of combining biological observations with nonlinear, non-Gaussian ecosystem models using a probabilistic, or Bayesian, approach has been recently suggested to predict bivalve-environment (mussel– plankton–detritus) interactions and production carrying capacity for coastal areas (D”
Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo (2007, methodology): “The statistical framework for ecological data assimilation is provided by the nonlinear and non-Gaussian state space model (e.g. Dowd and Meyer, 2003), xt ¼ ftðxt−1; ht; ntÞ ð1Þ yt ¼ htðxt;/t; vtÞ ð2Þ defined for t=1,… T.”