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Gigante, G. et al. Network events on multiple space and time scales in cultured neural networks and in a stochastic rate model.
Therefore, this paper introduces a technique to control packet level congestion in distributed network by adopting a novel metric termed as stochastic rate control.
where denotes the drift, is the diffusion, and is the vector of (generally unknown) parameters (i.e., the elements of are the stochastic rate constants ).
In this paper, we consider models of GRN based on chemical master equations and study the problem of estimating stochastic rate constants therein.
Section 3 presents the particle filtering algorithm for the estimation of the stochastic rate constants and compares its performance with prior work.
For instance, relying on (1) to infer the parameters of the network (i.e., the stochastic rate constants ) may in principle be possible [13]; however, it is computationally rather intensive to do so.
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Therefore, the construction of the stochastic rate-based model is phenomenological in nature.
Yet, the H a, s, yr; w = 0) estimator occasionally performed poorly in the Stochastic Rates scenario.
In addition, trend estimates for the last 4 5 years of the time series were highly variable for the H a, s, yr) sub-models in the Stochastic Rates and Kitchen Sink scenarios (Figure 3).
One exception was the H a, s, yr; w = 0) estimator in the Stochastic Rates scenario, where the model was occasionally unstable, resulting in large initial abundances and negative values of log λt) (Figure S1).
Importantly, adding the mark-recapture abundance estimates to the objective function substantially improved the performance of the H a, s, yr) sub-model in the Stochastic Rates scenario (Figure 3) and also resulted in less biased estimates of mean abundance for both sets of model estimators in all scenarios (Table S1).
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