javascriptalgorithmrandomdistributiongaussian

JavaScript Math.random Normal distribution (Gaussian bell curve)?

I want to know if the JavaScript function Math.random uses a normal (vs. uniform) distribution or not.

If not, how can I get numbers which use a normal distribution? I haven't found a clear answer on the Internet, for an algorithm to create random normally-distributed numbers.

I want to rebuild a Schmidt-machine (German physicist). The machine produces random numbers of 0 or 1, and they have to be normally-distributed so that I can draw them as a Gaussian bell curve.

For example, the random function produces 120 numbers (0 or 1) and the average (mean) of these summed values has to be near 60.

Solution

  • I want to know if the JavaScript function Math.random is normal distribution or not

    Javascript Math.random is not a Normal Distribution(Gaussian bell curve). From ES 2015, 20.2.2.27 "Returns a Number value with positive sign, greater than or equal to 0 but less than 1, chosen randomly or pseudo randomly with approximately uniform distribution over that range, using an implementation-dependent algorithm or strategy. This function takes no arguments." So the provided collection when n is high enough we will get approximately uniform distribution. All values in the interval will have equal probability of appearance(straight line parallel to the x axis, denoting number between 0.0 and 1.0).

    how can I get numbers which are normal distribution

    There are several ways of getting collection of numbers with a normal distribution. As answered by Maxwell Collard the Box-Muller transform does transform uniform distribution to normal distribution(the code can be found in Maxwell Collard answer).

    An answer to another stackoverflow answer to a question has a reply with other uniform distribution to normal distribution algorithms. Such as: Ziggurat, Ratio-of-uniforms, Inverting the CDF Besides one of the answers says that: says:

    The Ziggurat algorithm is pretty efficient for this, although the Box-Muller transform is easier to implement from scratch (and not crazy slow).

    And finally

    I want to rebuilt a Schmidt-machine (German physicist), the machine produces random numbers of 0 or 1 and they have to be normal distributed so I can draw them in Gaussian bell curve.

    When we have only two values (0 or 1) Gaussian curve looks the same as uniform distribution with 2 possible values. That is why a simple 

    function randomZero_One(){
    return Math.round(Math.random());
}

    would suffice. It would return pseudo-randomly with approximately equal probability values 0 and 1.