GitHub

Generate and plot sample of Uniform, Normal, Binomial, Exponential, and Standard Normal random variables using methods including LGM, Box-Muller, Polar-Marsaglia.

We want to generate 10,000 Uniformly distributed random number on [0,1] using LGM and compare with built-in python function. LGM empirical mean = 0.4996 LGM empirical standard deviation = 0.2872 Built ...
Europe PMC

On bounds in Poisson approximation for distributions of independent negative-binomial distributed random variables.

Using the Stein-Chen method some upper bounds in Poisson approximation for distributions of row-wise triangular arrays of independent negative-binomial distributed random variables are established in ...
GitHub

06_common.md

Remember the goal of probability modeling. We are assuming a probability distribution for our population as a way of parsimoniously characterizing it. In fact, the normal distribution only requires ...