BinomialDistribution[n, p] represents a binomial distribution with n trials and success probability p.

Distributions defined in this package have been added to the built-in Mathematica kernel. The input syntax for DiscreteUniformDistribution has changed. Random and RandomArray ...

There are many types of distributions that are relevant for communication systems. In telecom, for instance, exponential and Erlang distributions modeling talk lengths are ...

Bounded domain distributions naturally come up when random variables should only vary in a finite interval. Some distributions, like beta, occur in a variety of ways, ...

Urn models have a long history, starting with Laplace suggesting in 1786 that France's population be estimated by an urn-sampling scheme. They are conceptually relatively ...

Mathematica's sophisticated algorithms for handling higher mathematical functions to arbitrary precision—and in symbolic form—immediately brings a new level of accuracy—and ...

In almost every area where probability and statistics are used there have been found a few parametric distribution families that are known to be good models. The origins vary ...

The functions described here are among the most commonly used discrete univariate statistical distributions. You can compute their densities, means, variances, and other ...

Discrete distributions come from a variety of backgrounds, but perhaps the most common relate back to the simple Bernoulli trial, which chooses between two outcomes, called ...

MultinomialDistribution[n, {p_1, p_2, ..., p_m}] represents a multinomial distribution with n trials and probabilities p_i.