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Generating Biased Random Numbers


Instead of producing a series of numbers distributed uniformly over an interval, we need data following one of the classical distributions such as the normal distribution (i.e. the numbers should give a "bell curve").


The library solution

The easy solution is use the "random.plt" library from PLaneT.

Let's try generating numbers with a Gaussian distribution (a normal distribution with mean 0 and standard deviance 1):

500 Can't connect to (connect: Connection refused)

If we want to mimick a stocastic variable, we can use random-source-make-gaussians and a source of random bits.

500 Can't connect to (connect: Connection refused)

Alternatively, one could simple define X as 500 Can't connect to (connect: Connection refused)

The do it your self solution

Given a distribution, lookup an algorithm in a statistics reference. If you can't find an algorithm, consult a numerical analyst.

Let's examine the case of the normal distribution. The two parameters mu (mean) and sigma (standard deviance) determines a specific normal distribution.

500 Can't connect to (connect: Connection refused)

An example of usage: 500 Can't connect to (connect: Connection refused)

If you are unsatisfied with the fact that you get the exact same numbers as above, then randomize the source of random numbers: 500 Can't connect to (connect: Connection refused)


The algorithm used is the polar Box Muller method. The algorithm takes two independent uniformly distributed random numbers between 0 and 1 (represented in the code as (random-real)) and generates two numbers with a mean of my and standard deviation sigma. The method produces two numbers at a time, so since we only need one, the second is saved for later in the variable next.

Comments about this recipe

Note that the Perl Cookbook includes an interesting discussion of converting a set of values (and weights) into a distribution. This should also be converted to Scheme and shown here.

Mathematically-inclined Schemers should also take a good look at random.ss, which contains these and many other statistical methods.

It's also worth noting that if a bell-curve type thing is all you're looking for, generating two or more random numbers and taking the average will tend to favor the middle values. For example, consider a pair of dice: there is exactly one combination out of 36 that yields 2 and one that yields 12 (the outlying values), while there are six combinations that yield 7 (the center value). You could also use a weighed average to reduce the effect if averaging two random numbers produces a bell curve which is too steep for your application.


-- BrentAFulgham - 14 May 2004

-- JensAxelSoegaard - 01 Jun 2004 -- JensAxelSoegaard - 12 Dec 2006

[TODO: Move the following remarks to another recipe]

If you wish to randomly select from a set of weights and values, convert the weights into a probability distribution, then use the resulting distribution to pick a value.

If you have a list of weights and values you want to randomly pick from, follow this two-step process: First, turn the weights into a probability distribution with weight_to_dist below, and then use the distribution to randomly pick a value with weighted_rand:

[TODO: Use the random-source-make-discretes from random.ss to solve the above problem]

TopicType: Recipe
ParentTopic: NumberRecipes
TopicOrder: 100

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