Discrete Distributions

Compute, fit, or generate samples from integer-valued distributions

A discrete probability distribution is one where the random variable can only assume a finite, or countably infinite, number of values. For example, in a binomial distribution, the random variable X can only assume the value 0 or 1. Statistics and Machine Learning Toolbox™ offers several ways to work with discrete probability distributions, including probability distribution objects, command line functions, and interactive apps. For more information on these options, see Working with Probability Distributions.

Categories

Binomial Distribution
Fit, evaluate, and generate random samples from binomial distribution
Geometric Distribution
Evaluate and generate random samples from geometric distribution
Hypergeometric Distribution
Evaluate the hypergeometric distribution or its inverse, generate pseudorandom samples
Multinomial Distribution
Evaluate the multinomial distribution or its inverse, generate pseudorandom samples
Negative Binomial Distribution
Fit parameters of the negative binomial distribution to data, evaluate the distribution or its inverse, generate pseudorandom samples
Poisson Distribution
Fit, evaluate, and generate random samples from Poisson distribution
Uniform Distribution (Discrete)
Evaluate the discrete uniform distribution or its inverse, generate pseudorandom samples