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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