We also assume that the analog signals X (t) transmitted from source follows the

Gaussian distribution N (0, [[sigma].sup.2.sub.x]), the channels between source and relays as well as relays and destination are all AWGN channels, the noises in relays and destination are i.i.d.

However, our previous simulations showed better simulation results for the average D2D throughput in a

Gaussian distribution model, which is a non-uniform realistic distribution model, than the results of a uniform distribution model.

Base period 1941-2010; (d) Same as (c), but for 5-year running mean; (e) Return period curve obtained by inverting the fit of annual sum of monthly mean precipitation to a

Gaussian distribution that scales with the smoothed global mean surface temperature.

The conjugate prior distribution for the covariance matrix of multivariate

Gaussian distribution is usually chosen as Wishart or inverse-Wishart distributions (Kass and Natarajan 2006; Murphy 2007).

We refer to the model described above, where the pseudorapidities obey a

Gaussian distribution, as the G model.

In this section, we try to use the proposed

Gaussian distribution curve to estimate the settlement curve of the ground surface under the effect of enlarging the existing rectangular underpass.

where P([q.sub.k]|X, [[theta].sub.k]) is the posterior probability, in this case, the class [q.sub.k] given a sample X or a feature vector following a

Gaussian distribution with parameters [[theta].sub.k]; p(X|[q.sub.k], [[theta].sub.k]) is the likelihood of the sample X, given a class [q.sub.k] following a

Gaussian distribution with parameters [[theta].sub.k]; and p([q.sub.k]) is the probability that the class [q.sub.k] is presented.

We approximate local posterior as a

Gaussian distribution and fuse the local posterior via a max-consensus protocol.

If we assume that the parameters [[[[omega].sub.k.sup.f].sub.n], [[omega].sub.k.sup.b]].sub.n] are subject to independent and identical

Gaussian distribution [mathematical expression not reproducible], as shown in Figure 6(b), then (6) can be sampled from F([rho](t)) in (13) condition on [z.sub.t] = k:

The standard cosmological model explains density fluctuations in matter in the early universe based on

Gaussian distribution.

Its skewness and kurtosis coefficients are different from the 0 and 3 values of a

Gaussian distribution. The approach of Section 2.1.1 is used to characterize u(t) based on its four moments and the autocorrelation function using Polynomial Chaos Expansion (PCE).

Socrates points out the logical flaws as well as the fundamentally false assumption that risk factors have a symmetrical

Gaussian distribution.