
TY  - JOUR
A1  - Jones, Emlyn 
A1  - Parslow, John 
A1  - Murray, Lawrence 
T1  - A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model
JF  - Australian Meteorological and Oceanographic Journal
JO  - Aust. Meteor. and Ocean. J.
Y1  - 2010
VL  - 59
IS  - 1
SP  - 7
EP  - 16
KW  - 
UR  - https://doi.org/10.1071/ES10003 
N2  - Complex marine biogeochemical (BGC) models are now being used to inform management decisions at a variety of scales, from local coastal management issues through to the global effects of climate change. A majority of BGC models are still deterministic in nature with model tuning and calibration performed in a heuristic manner. This method does not allow for a quantitative estimate of model or parameter uncertainty. If these models are reformulated in a physical-statistical framework, using a stochastic process model, formal state and parameter estimation routines can be implemented, yielding quantitative estimates of model uncertainty. We have performed twin experiments using an idealised stochastic-dynamic non-linear phytoplankton-zooplankton model to trial two Markov Chain Monte Carlo (MCMC) Algorithms. The first uses a Particle Filter (PF) with a MetropolisHastings (MH) update step for state-estimation embedded within a MH MCMC for hyper-parameter estimation; we have named this approach MH-PF-MH. The second approach uses Gibbs sampling for state estimation and MH MCMC over hyper-parameters; referred to as MH-Gibbs. Both algorithms performed well in the twin-experiments, allowing both state and parameter estimation. The hybrid MH-Gibbs is more efficient than the MH-PF-MH algorithm, forming a reliable posterior sample with up to 99.9% fewer model trajectories. However, the MHPF-MH algorithm is expected to be more flexible in its implementation. 
ER  -

