These models, typically based on systems of ordinary or stochastic differential equations, can generate realistic behavior [7].
Making certain individual-level modeling assumptions (e.g., the mass-action principle of infectious spread, time independent infection rates, etc)., theorists construct models (typically) based on Ordinary Differential Equations (ODEs) which describe the dynamics of the expected population size in different disease stages without tracking individuals.
The selection of segments for the Exponential model is typically based on the shape of the curve on a plot of square root of isopach area versus deposit thickness.
These audio-visual inversion models are typically based upon Hidden Markov Models (HMMs) [17, 18], neural networks [19], or other lookup models [20].
At the continental scale, avian distribution models are typically based on temperature- and precipitation-based bioclimatic variables [28], [29].
The representation of crowd movement in existing evacuation models is typically based on data collected in the 1950s to 1980s, i.e., data that are more than 40 years old.
Systems biology models are typically based on the mechanistic understanding of the systems.
Phenomenological models are typically based on some variation of a persistent random walk biased in the direction of the chemokine gradient (Weiner 2002).
Visual 'icon-based' modelling environments, typically based on system dynamics, can help to reveal model structure and reduce the 'black box' syndrome (Smith et al., 2005).
In a Bayesian setting, model selection is typically based on the Bayes factor, which measures the amount of evidence the data provides for one model over another [ 40, 41].
