Uniform Distribution (Continuous)

Evaluate and generate random samples from continuous uniform distribution

Statistics and Machine Learning Toolbox™ offers several ways to work with the uniform distribution.

• Create a probability distribution object UniformDistribution by specifying parameter values. Then, use object functions to evaluate the distribution, generate random numbers, and so on.

• Use distribution-specific functions with specified distribution parameters. The distribution-specific functions can accept parameters of multiple uniform distributions.

• Use generic distribution functions (cdf, icdf, pdf, random) with a specified distribution name ('Uniform') and parameters.

To learn about the uniform distribution, see Uniform Distribution (Continuous).

Objects

 UniformDistribution Uniform probability distribution object

Apps

 Probability Distribution Function Interactive density and distribution plots

Functions

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Create UniformDistribution Object

 makedist Create probability distribution object

Work with UniformDistribution Object

 cdf Cumulative distribution function icdf Inverse cumulative distribution function iqr Interquartile range mean Mean of probability distribution median Median of probability distribution pdf Probability density function random Random numbers std Standard deviation of probability distribution truncate Truncate probability distribution object var Variance of probability distribution
 rand Uniformly distributed random numbers unifcdf Continuous uniform cumulative distribution function unifpdf Continuous uniform probability density function unifinv Continuous uniform inverse cumulative distribution function unifit Continuous uniform parameter estimates unifstat Continuous uniform mean and variance unifrnd Continuous uniform random numbers
 mle Maximum likelihood estimates
 disttool Interactive density and distribution plots qqplot Quantile-quantile plot randtool Interactive random number generation

Topics

Uniform Distribution (Continuous)

The uniform distribution (also called the rectangular distribution) is notable because it has a constant probability distribution function between its two bounding parameters.

Generate Random Numbers Using Uniform Distribution Inversion

This example shows how to generate random numbers using the uniform distribution inversion method.