Documentation

# 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.

• Binomial Distribution
Fit parameters of the binomial distribution to data, evaluate the distribution or its inverse, generate pseudorandom samples
• Geometric Distribution
Evaluate the geometric distribution or its inverse, generate pseudorandom samples
• 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 parameters of the Poisson distribution to data, evaluate the distribution or its inverse, generate pseudorandom samples
• Uniform Distribution (Discrete)
Evaluate the discrete uniform distribution or its inverse, generate pseudorandom samples