Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo

75 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deriving optical metrics of coastal phytoplankton biomass from ocean colour (2012, unknown): “This feature of the models to evolve as more data is acquired may lend itself well to integration into ecosystem modelling approaches such as state-space models, e.g. Dowd (2007).”
A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model (2010, methodology): “2003) and Markov Chain Monte Carlo (MCMC) algorithms (Dowd 2006; Dowd 2007; Arhonditsis et al. 2008).”
Assimilation of remote sensing into crop growth models: Current status and perspectives (2019, methodology): “A number of implementations of these filters are straightforward extensions of the basic MCMC algorithms (Dowd, 2006, 2007), while others are more directly based on importance sampling (van Leeuwen, 2009; Ristic et al., 2004; Arulampalam et al., 2002).”
Bayesian Solution Uncertainty Quantification for Differential Equations (2013, background): “…inverse problem concerns the uncertainty in unknown model inputs, θ , given measurements of the model states (Bock, 1983; Ionides et al., 2006; Dowd, 2007; Ramsay et al., 2007; Xue et al., 2010; Brunel, 2008; Liang and Wu, 2008; Calderhead and Girolami, 2011; Campbell and Steele, 2011;…”