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Online Pseudo Random Number Generator

This online tool generates pseudo random numbers based on the selected algorithm. A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG) is an algorithm for generating a sequence of numbers that approximates the properties of true random numbers. The online pseudo random number generator supports:



  • Mersenne Twister
  • WELL (Well Equidistributed Long-period Linear)
  • CryptMT
  • PRBS
  • BlumBlumShub
  • Uniform
  • Gaussian (Standard Normal Distribution)
  • Poisson
  • Gamma and more...
Enjoy generating pseudo random numbers!

This tool is not qualified for cryptographic uses and is not cryptographically secure. You should not rely on it in security-sensitive situations.

Select one of the uniform random number generators as the random number source.
Seed?Seed Seed value to be used to initialize the pseudo random number generator before the calculation starts.

If Random is checked, a random seed value will be generated based on user interaction and network properties.

The simple randomization algorithm is not documented here to prevent the affect of user behavior on randomization nature.
The algorithm can be extracted from the source of this page if needed.

Using a large number as seeed is recommended as certain generators internal states takes some time to start generating random numbers.

Seed can not be 0 for PRBS generator. In general avoid 0 as seed value.

When continuing, seed value is not used and the random number generator use the previous state.
Range Min?Range MinMinimum value of the generated random numbers. Integer or decimal. Included.
Applies to both Uniform or any other distribution.
It is a window of to output infinite distribution.

It does not change the distribution function, but limits the output to show
the part of distribution which falls between min and max.
Range Max?Range MaxMaximum value of the generated random numbers. Integer or decimal. Excluded.
Number of outputs?NumberNumber of pseudo random numbers to be generated. Max 64 per request.
If you need bigger chunks of random number generation, please contact me.
Continue button will allow generation of higher number sequences in smaller chunks.
Plot CDF?Cumulative Distribution functionPlots the Cumulative Distribution Function diagram of the generated numbers

CalculateInitialize the state of the pseudo random number generator
with the seed value and starts to generate new random numbers.
ContinueUses the current state of the pseudo random number generator
to continue generating new random numbers.

Calculation Time: 0 ms
Print Time: 0 ms

Generated random numbers
The output random numbers generated based on the selections. Use them wisely.


Algorithm to be used to generate the pseudorandom numbers


Algorithm to be used to distribute number randoms
The following table denotes the acceptable strings for name, as well as the parameters for that distribution:
name Distribution Input Parameters
'beta' or 'Beta' Beta Distribution a b
'bino' or 'Binomial' Binomial Distribution n: number of trials p: probability of success for each trial
'birnbaumsaunders' Birnbaum-Saunders Distribution β γ
'burr' or 'Burr' Burr Type XII Distribution α: scale parameter c: shape parameter k: shape parameter
'chi2' or 'Chisquare' Chi-Square Distribution ν: degrees of freedom
'exp' or 'Exponential' Exponential Distribution μ: mean
'ev' or 'Extreme Value' Extreme Value Distribution μ: location parameter σ: scale parameter
'f' or 'F' F Distribution ν1: numerator degrees of freedom ν2: denominator degrees of freedom
'gam' or 'Gamma' Gamma Distribution a: shape parameter b: scale parameter
'gev' or 'Generalized Extreme Value' Generalized Extreme Value Distribution k: shape parameter σ: scale parameter μ: location parameter
'gp' or 'Generalized Pareto' Generalized Pareto Distribution k: tail index (shape) parameter σ: scale parameter μ: threshold (location) parameter
'geo' or 'Geometric' Geometric Distribution p: probability parameter
'hyge' or 'Hypergeometric' Hypergeometric Distribution M: size of the population K: number of items with the desired characteristic in the population n: number of samples drawn
'inversegaussian' Inverse Gaussian Distribution μ λ
'logistic' Logistic Distribution μ σ
'loglogistic' Loglogistic Distribution μ σ
'logn' or 'Lognormal' Lognormal Distribution μ σ
'nakagami' Nakagami Distribution μ ω
'nbin' or 'Negative Binomial' Negative Binomial Distribution r: number of successes p: probability of success in a single trial
'ncf' or 'Noncentral F' Noncentral F Distribution ν1: numerator degrees of freedom ν2: denominator degrees of freedom δ: noncentrality parameter
'nct' or 'Noncentral t' Noncentral t Distribution ν: degrees of freedom δ: noncentrality parameter
'ncx2' or 'Noncentral Chi-square' Noncentral Chi-Square Distribution ν: degrees of freedom δ: noncentrality parameter
'norm' or 'Normal' Normal Distribution μ: mean σ: standard deviation
'poiss' or 'Poisson' Poisson Distribution λ: mean
'rayl' or 'Rayleigh' Rayleigh Distribution σ: scale parameter
'rician' Rician Distribution s: noncentrality parameter σ: scale parameter
't' or 'T' Student's t Distribution ν: degrees of freedom
'tlocationscale' t Location-Scale Distribution μ: location parameter σ: scale parameter ν: shape parameter
'unif' or 'Uniform' Uniform Distribution (Continuous) a: lower endpoint (minimum) b: upper endpoint (maximum)
'unid' or 'Discrete Uniform' Uniform Distribution (Discrete) N: maximum observable value
'wbl' or 'Weibull' Weibull Distribution λ: scale parameter k: shape parameter
Random Number GeneratorsRandom Number Generatorsonline Random number generator is an online randomizer which online random picker generates the online random generator
online random password generator differs from online pseudo random generator
Pseudorandom definition and pseudorandom generator are both pseudorandom function
Pseudorandom permutation of pseudorandom code calculates pseudorandom numbers in simulations
Pseudorandom numbers affect the accuracy of a simulation provides pseudorandom data
Pseudorandom generator definition is a way of resulting pseudorandom generator however not pseudorandom generator bibtex.
Simple pseudo random number generator runs random number generator algorithm.
deterministic random bit generators are prng algorithm
Online mersenne twister seed and mersenne twister example are generated in mersenne twister excel.
Mersenne twister javascript is faster than mersenne twister c#.
Mersenne twister a 623-dimensionally equidistributed uniform pseudorandom number generator
Mersenne twister table correlates mersenne twister visual basic of mersanne twister.
The following algorithms are pseudorandom number generators:
ISAAC (cipher) with substantial Lagged Fibonacci generator forms Linear congruential generator.
- The most common type in computer programming languages for Linear feedback shift register, Maximal periodic reciprocals, and Mersenne twister contains Multiply-with-carry. However Naor-Reingold Pseudorandom Function complies with Park–Miller random number generator under RC4 PRGA rules.
Well Equidistributed Long-period Linear uses Xorshift in Hardware (True) Random Number Generators (TRNGs).
Test speed of Box-Muller method vs just adding three random numbers. Create a gaussian random number distribution using the java Random. This site describes functions for generating random variates and computing their probability distributions.

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