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Bénard Marangoni convection at large Marangoni and low Prandtl numbers can be efficiently simulated using spectral numerical methods.
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This approach of noisy gene regulation can be efficiently simulated in continuous modeling approaches by using chemical master equations (CMEs) and Gillespie's algorithm (Gillespie, 1976, 1977; Gonze and Goldbeter, 2006; Rao et al., 2002; Schultz et al., 2007).
It is well known (see, e.g., [35 37]) that Rayleigh fading channel models, characterized by the complex Gaussian process μ(t), can be modeled and efficiently simulated using the SOC process μ ̂ ( t ) = ∑ n = 1 N c n e j ( 2 π f n t + θ n ), (3).
We describe how data can be efficiently simulated under the model, which is crucial to allow the use of approximate Bayesian computation (ABC) techniques (Pritchard et al. 1999; Beaumont et al. 2002) to estimate parameters.
A stochastic pi-calculus model can be efficiently simulated in a rigorous way that is faithful to chemical interactions.
Perturbations were also simulated using SQUAD.
In this article, the estimated power spectrum density is analyzed and simulated using both nested sampling and coprime sampling structures, which provide us a new way to efficiently use the spectrum.
The rest of the large protein is simulated using less demanding classical physics.
Spatial coordinates were simulated using Gaussian distributions.
Missing data were simulated using multiple imputation.
These results were simulated using pseudo ORFs.
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