codes from theorems in the field of stochastic processes
1.Central limit theorem --> The central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed. (source: wiki)
2.law of large numbers --> The law of large numbers states that the cumulative average probability of an event approaches the expected value as the number of trials approaches infinity. (source: wiki)
3.rayleigh distribution --> The Rayleigh distribution is a special case of the Weibull distribution. If A and B are the parameters of the Weibull distribution, then the Rayleigh distribution with parameter b is equivalent to the Weibull distribution with parameters A = 2 b and B = 2. (source: wiki)
4.Poisson probability --> computes the Poisson probability density function at each of the values in x using the rate parameters in lambda. x and lambda can be scalars, vectors, matrices, or multidimensional arrays that all have the same size. If only one argument is a scalar, poisspdf expands it to a constant array with the same dimensions as the other argument. (source: wiki)