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The parametric method can only be used for specific noise distributions such as Gaussian, exponential, uniform, and delta distributions.
This category contains a diverse set of parametrised sources including Bernoulli, Binomial, Poisson, exponential, uniform, and normal distributions.
In some scenarios, however, other distributions, e.g., an exponential, uniform, or Laplacian distribution, seem to be more reasonable to describe the model in (5) [22 24].
In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal) for the lifetime of a single sensor.
Again, pairs of samples were generated from exponential, uniform, and normal distributions.
Equally sized samples were drawn from exponential, uniform, and normal distributions.
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In this study, we consider four deployment distributions: grid, normal, exponential and uniform.
By using an impulsive delayed differential inequality and Lyapunov function, several exponential and uniform stability criteria of the impulsive delayed memristor-based recurrent neural networks are obtained.
Since the Cumulative Density Function (CDF) of a Weibull distribution is log-concave and the CDFs of many common distributions (e.g., Exponential distribution, Uniform distribution, etc).
The distribution of case group was considered as exponential, normal, uniform, cauchy distribution, complex of triangular, normal, and exponential distribution, or mixture of normal distribution, respectively, when the distribution of control group was regarded as normal distribution with mean as 1 and standard deviation as 1.5.
The cluster model (normal, exponential or uniform) was drawn randomly.
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