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This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability.
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This is generally done to improve the precision of model parameter estimates.
Aiming at the speed control of ultrasonic motor, the influence of the parameter's initial values on the precision of model is discussed.
Constraining the prior model against the species time histories within the measurement uncertainties led to notable improvements in the precision of model predictions against the species data as well as the global combustion properties considered.
The parameter fixing was undertaken to improve the precision of model parameters [21, 56].
The coefficients of functions and lack of fitness tests were used for determining the degree of precision of model.
The transition functions for mortality and basal area were fitted simultaneously, using the full information maximum likelihood, which produced consistent and efficient estimates contributing to increased precision of model predictions.
Here the transition functions for mortality and basal area were fitted simultaneously using the full information maximum likelihood (FIML) in order to obtain estimates that are consistent and efficient and contribute in increased precision of model predictions.
These refinements provided improved precision of model output.
To balance the thoroughness of configurational sampling and precision of model representation, we represented the structures in a multi-scale fashion.
It would be advantageous to estimate recall error in populations using these and similar methods, as this would improve the precision of model selection.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com