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The most frequently used time scale for this variability ranges from months to years to decades.
To check for bistable dynamics, we first used time scale separation to develop a reduced order model (Methods, Additional file 1).
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As time scale MILP control is computationally much more efficient, we propose to use time scale MILP control.
For instance, Ullah, Bruno and Pearson [54] proposed model simplification by the elimination of nodes with low equilibrium occupancy probability using time scale separation arguments.
This function fills the gap between existing R packages for simulating gap time models (covered by complex.surv.dat.sim [ 33]) and for simulating inhomogeneous Poisson processes using time scale transformations (covered by NHPoisson [ 34]).
Analyzing the data at the population level permits derivation of the most likely common disease trajectory using time scale adjustment and back- and forth extrapolation between AD and MCI patients.
Infinite horizon problems can also be modeled using time scales tools.
The aim of the present work is to apply some of those results to economic models and to show advantages of using time scales tools.
Unless otherwise stated, we use time-scaling factor λ = 1,000, time-scaled population size 2 N = 1,000, and mutation rate µ = 10−.
The first method uses time-frequency analysis and the second one uses time-scale analysis.
Because the full-order model is ill-conditioned, reduced-order models are constructed using time-scale decomposition arguments.
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