Many random processes that occur over a long time with large sample sizes, from processes that produce school grades to height among a population, converge on the common Gaussian distribution
. "It's a very reasonable default expectation," says Santa Fe Institute Omidyar Fellow Andy Rominger.
Assume that X(t) follows Gaussian distribution
N(0, [s.sup.2.sub.x]), and the channels between source and relays as well as relays and destination are all additive white Gaussian noise (AWGN) channels.
Continuous variables with Gaussian distribution
of both study and control groups were compared using Independent student's t-test which included age, BMI, lipid profile and fasting blood glucose (FBG).
6, where the average D2D throughput for the Gaussian distribution
shows better results than that of the random distribution.
One of the early models of the modern era (when computers where in their infancy), postulated that market returns could be modelled using a "Gaussian distribution
," an elegant probabilistic model successfully applied in the natural science domain.
Return periods were obtained by inverting the fit of annual accumulation of monthly mean precipitation to a Gaussian distribution
that scales with the smoothed global mean surface temperature (GMST).
where the noise terms follow a Gaussian distribution
: [[epsilon].sub.1.sup.i], [[epsilon].sub.1.sup.i] [??] N(0, [[sigma].sup.2.sub.noise]).
The original Landau hydrodynamic model is, to our best knowledge, the only model suggesting some certain shape for pseudorapidity distributions of produced particles in both nucleon-nucleon and nucleon-nucleus collisions at very high energies, Gaussian distribution
. Of course, it is necessary to note that only in the case of a very high multiplicity does the pseudorapidity distribution of produced particles in an individual event become a meaningful concept.
It is widely reported that the settlement curve for a single tunnel takes the shape of an inverted Gaussian distribution
curve symmetrical to the tunnel axis at right angles .
Using an outlier algorithm that helped our algorithm to be correctly trained as the models used are based on Gaussian distribution
, outliers can affect greatly the performance of the model.
In the distributed particle filter proposed in [13, 14], the local posterior probability density function (PDF) is approximated by a Gaussian distribution
. Then the local PDF parameters are fused into the global posterior PDF's parameters by average consensus.
So (1) is rewritten as where [rho](x, t) is a stochastic variable denoting the traffic density in Cell(x) at time t, F([rho](x, t))is the SFD, and [e.sub.t] is process noise that is mode-specific with Gaussian distribution
(with mean zero and covariance [summation]).