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A comparative analysis of modeling methods is made to understand the predictability of each model.
Because of the increasing diversity of data sets and measurement techniques in biology, a growing spectrum of modeling methods is being developed.
A disadvantage of these multimodal Gaussian modeling methods is that they require pre-defined parameters such as the number of the Gaussian distributions and the standard deviations of those distributions.
A comparison of different modeling methods is presented in the following section.
The main category of the forward modeling methods is based on the radiative transfer equation (RTE).
To investigate how programs of gene expression relate to the phenotypic attributes of the macrophage, the development of in silico modeling methods is needed.
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The review also shows that in geomechanical modeling, continuum modeling methods are predominant compared with discontinuum methods.
Then, the advantages of the proposed hybrid models against "singular" modeling methods are demonstrated.
Mass reconstruction or modeling of these lenses will be needed, but current modeling methods are time intensive for specialists and expert human resources do not scale.
If modeling methods are robust at different scales, a series of neighborhood-scale models may be used to compare the health impacts of transportation decisions in neighborhoods with different socioeconomic conditions and may reveal disproportionate impacts.
Diverse environmental variables can be used as predictor variables, and modeling methods are under continual development and include recent innovations that allow modelers to combine both survey data and opportunistically collected records (Dorazio 2014; Fithian et al. 2014).
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