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RandomReal—Wolfram Language Documentation

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• See Also
  • RandomVariate
  • RandomInteger
  • RandomComplex
  • RandomChoice
  • RandomPoint
  • SeedRandom
  • FindInstance
• Related Guides
  • Random Number Generation
  • Statistical Data Analysis
  • Mathematical Functions
  • GPU Computing
  • GPU Programming
  • GPU Computing with NVIDIA
  • GPU Computing with Apple
• Tech Notes
  • Pseudorandom Numbers
  • Random Number Generation
  • Continuous Distributions

RandomReal[]

gives a pseudorandom real number in the range 0 to 1.

RandomReal[{x_min,x_max}]

gives a pseudorandom real number in the range x_min to x_max.

RandomReal[x_max]

gives a pseudorandom real number in the range 0 to x_max.

RandomReal[range,n]

gives a list of n pseudorandom reals.

RandomReal[range,{n₁,n₂,…}]

gives an n₁×n₂×… array of pseudorandom reals.

• RandomReal[{x_min,x_max}] chooses reals with a uniform probability distribution in the range x_min to x_max.
• RandomReal[spec,WorkingPrecision->n] yields reals with n-digit precision. Leading or trailing digits in the generated number can turn out to be 0.
• RandomReal gives a different sequence of pseudorandom reals whenever you run the Wolfram Language. You can start with a particular seed using SeedRandom.
• A Method option to SeedRandom can be given to specify the pseudorandom generator used.

A random real number in the range 0 to 1:

A random real number in the range to :

A random real number in the range 0 to 10:

Five random reals in the range 0 to 1:

A 3×2 array of random reals in the range to :

Random coordinates for 4 points in 3 dimensions:

Generate random reals of any magnitude:

Generate random reals of any precision:

Generate low-precision reals:

Generate a random real with 50-digit precision:

A random walk:

Circles at random positions:

Random array of gray levels:

Spheres at random positions:

Two-dimensional random walk:

Three-dimensional random walk:

Determinants of random 100×100 matrices:

Generate a complex number in the unit square:

Generate 5 complex numbers:

Use RandomComplex instead:

Generate exponential random variables using inversion method:

Compare sample histogram with the PDF of ExponentialDistribution:

Alternatively, use RandomVariate to sample from nonuniform distributions directly:

Test whether both datasets could be samples from the same distribution:

Construct a surface from random heights:

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