Exact(6)
end{cases} (3) It is interesting to see that the authors gave a complete threshold for any size of noise.
The size of noise ε(t) in (10) is 0.5, defined by the standard deviation.
The expected size of noise components can be derived using random graph theory (Coupier et al., 2005).
Interestingly, comparison of equations (29) and (30) shows the opposite for Hill-type kinetics: the size of noise predicted by the heuristic CME underestimates the true value.
In contrast the conventional heuristic SSA is shown to overestimate the size of noise for Michaelis-Menten kinetics, considerably under-estimate the size of noise for Hill-type kinetics and in some cases even miss the prediction of noise-induced oscillations.
The observed SD2-T1 and SD2-T2 are constructed based on SD2-D0 by adding noise sampled respectively from a normal distribution with means zero and standard deviation q and from a uniform distribution from zero to q, where q is the parameter to control the size of noise.
Similar(54)
It can be viewed as a tuning parameter controlling the size of the noise set because larger λ lead to smaller noise sets.
In particular, the LNA predicts that there exists a stress level for which the size of the noise is minimized while, in contrast, the IOS predicts the noise level to increase monotonically with stress.
It should be noted, however, that the least-squares fitting assumes simple Gaussian noise in log[Δ n c /Δ n], whereas in reality the size of the noise in log[Δ n c /Δ n] increases as Δ n decreases.
This was because the effect of the impulsive noise to the energy distribution became more distinct and easier to detect when the size of the noise was larger.
Because of the latter fact it is often referred to as the "size" of the noise.
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