The phrase "approximation allows to" is not correct in standard written English.
It should be followed by a noun or gerund to complete the thought, such as "approximation allows us to" or "approximation allows for."
Example: "The approximation allows us to simplify complex calculations."
Alternatives: "approximation enables" or "approximation permits."
This approximation allows to construct the horizontal lift of the Ornstein Uhlenbeck process on the path space through the Markovian connection.
It is shown that this approximation allows to analyze the optical properties of the Hg3X2Y2 (X = S, Se, Te; Y = F, Cl, Br, I) crystals.
Using this approximation allows to reduce the dimensionality of the search to two dimensions, which means a smaller computational cost and time because we only search for final heading angle and final time.
Although the proposed functions do not perfectly match the ACM rain event as it suffers from intensive fluctuations, we believe that the proposed sinusoidal approximation allows to represent the general situation in which the satellite capacity is reduced.
This seemingly crude approximation allows to obtain similar synchronization patterns as those observed with more refined models based on neuronal ensembles placed at each cortical area [23] [25].
The approximation allows to reduce the number of dynamical variables to the number of neural populations, and so it drastically reduces the computational cost associated with a scan in parameter space.
In the Figure 4b it can be seen that the percentages of DMNs (of the kind rs = 5 and f = 3) with metabolic cores grow with the number of subsystems for all λ values, and for n = 600 our approximations allow to observe that the networks, independently of their structural topology of fluxes and regulatory signals, acquire an only global structure with metabolic cores.
The proposed Gaussian function generator based on the new approximation function allows to extend its capability of generating any continuous mathematical functions, this feature being obtained by changing the approximation function coefficients.
Such a smooth Gaussian approximation allows us to use a truncated polynomial expansion to accurately approximate the density after the splitting event.
This approximation allows us to calculate the neuron's response to a train of presynaptic events with an iterative formula, and to calculate the discriminability based on intrinsic properties analytically (see Text S1).
This approximation allows us to consider the case τ ε =ε δ →0 and, moreover, to choose δ close to 2. The relations between the choice of δ and the multi-step MLE-processes are the following.
