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