Published
Edited
Aug 25, 2019
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Seat optimization by simulated annealing algorithmSIR simulation of infection (WebGL)Babylonian method visualized - computing sqrtSupertoroidUmbilic TorusBilinear interpolation of tileMultivariate Normal DistributionBroken Calabi-Yau Manifold 3DCircle InversionCalabi-Yau Manifold 3D
Box-Muller transform
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Statistics
// Generate n samples from standard normal distribution
function boxMuller(n) {
const samples = [];
Array(Math.ceil(n / 2)).fill().forEach(_ => {
const R = Math.sqrt(-2 * Math.log(Math.random()));
const theta = 2 * Math.PI * Math.random();
samples.push(R * Math.cos(theta)); // z1
samples.push(R * Math.sin(theta)); // z2
});
// if n is odd, drop the last element
return samples.slice(0, n);
}
normalSamples = boxMuller(10000)
import {chart as histogram} with {normalSamples as data} from "@d3/histogram"
import {chart as qqplot} with {normalSamples as data} from "@d3/normal-quantile-plot"
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Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
