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Nonstationary probability distribution models involve parameters that vary over time.
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The aim was to develop a stable model involving parameter optimization.
Such dynamical models involve unknown parameters, such as kinetic parameters, that one must guess from prior knowledge or estimate from experimental data in order to analyze and simulate the model.
Parameter identification is an important issue in computational biology since most of the models involve more parameters than the available data.
Models involve several parameters which are non-linear functions of the void fraction, e.g. relative permeabilities.
The available models involve many parameters that are very difficult to estimate.
This is because all the models involve unknown parameters or functions, which should be estimated from observations of the process.
The two models involve two parameters: a vapour-liquid partition constant and a free liquid-to-fluid mass transfer coefficient.
Many models involve tuning parameters for which values must be chosen.
Distributed parameter models involve far fewer unknown parameters than independent modal characteristics or finite element models.
Two distribution systems, the tanker system and the umbilical system, were analysed using two discrete event simulation models and involving parameter fitting values based on experimental data.
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