1 // Sample from a normal distribution with mean 0, stddev 1.

     2 function normal() {

     3   var x = 0, y = 0, rds, c;

     4   do {

     5     x = Math.random() * 2 - 1;

     6     y = Math.random() * 2 - 1;

     7     rds = x * x + y * y;

     8   } while (rds == 0 || rds > 1);

     9   c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform

    10   return x * c; // throw away extra sample y * c

    11 }

    12 

    13 // Simple 1D Gaussian (normal) distribution

    14 function normal1(mean, deviation) {

    15   return function() {

    16     return mean + deviation * normal();

    17   };

    18 }

    19 

    20 // Gaussian Mixture Model (k=3) fit using E-M algorithm

    21 function normal3(dd) {

    22   return function() {

    23     var r = Math.random(),

    24         i = r < dd[0][2] ? 0 : r < dd[0][2] + dd[1][2] ? 1 : 2,

    25         d = dd[i];

    26     return d[0] + Math.sqrt(d[1]) * normal();

    27   }

    28 }