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Analysis of the model and parameter identifiability using Markov chain Monte Carlo (MCMC) are presented.
The model and parameter estimates were used to predict the time when most subjects are expected to reach a stable SBP5mmHg, which was defined as SBP within 5 mmHg of systolic Prest.
Then the prior probabilities for the Bayesian network are quantified considering model and parameter uncertainties.
The impact of extreme model nonlinearity on model and parameter stability is also discussed, focusing on model identification aspects.
We also tested whether the model and parameter set produce dynamics consistent with those observed over the last half century.
This study presents an optimization method for designing cost-effective GCS monitoring networks under model and parameter uncertainty.
The performance of the hydrologic model and parameter replacement among sub-basins with hydrological similarity was verified by historical data.
This article investigates three-dimensional finite element models for structural dynamics problems with respect to both model and parameter uncertainties.
Application of the model and parameter values derived from the catalytic filter analysis to a SCR honeycomb situation constitutes a sort of indirect validation.
The solution includes the equivalent parameter domain for each model and parameter mapping that associates these two models on their equivalent parameter domains.
Of course, it will be the responsibility of the users of this new method to use it with the appropriate kinetic model and parameter values.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com