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 variableXcan 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, seeWorking 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 - 超几何分布
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