Module 7 · Probability Distributions and Simulation Basics Lesson 64 of 120

Uniform Distribution and Random Sampling

Scaling random draws for simulation without claiming uniform market behavior.

2:43 clip3:24:50–3:27:33 in the full courseWatch on YouTube

Transcript

19 sentences · select one to jump there

Check your understanding

Does a uniformly generated test fixture imply actual invoices are uniform?

Choose one answer

Code lab

Run it yourself

The lesson source in 7 languages. Edit it, run TypeScript and Python right here, and compare with the expected output.

064-uniform-distribution-and-random-sampling.ts
Start from GitHub
/**
 * Fintech Math Bootcamp · Lesson 064 of 120
 * Uniform Distribution and Random Sampling
 * Module 07: Probability Distributions and Simulation Basics
 *
 * Scenario: Scaling random draws for simulation without claiming uniform market behavior
 * Rule:     X = a + (b−a)U
 *
 * Try it:   Does a uniformly generated test fixture imply actual invoices are uniform?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/probability-distributions-and-simulation-basics/uniform-distribution-and-random-sampling/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

export function lesson064() {
  const a=100,b=200, draws=[0,.25,.5,.75];
  const samples=draws.map(u=>a+(b-a)*u);
  const result={samples,mean:(a+b)/2,variance:(b-a)**2/12};
  return result;
}

export const checkedResult = {"samples":[100,125,150,175],"mean":150,"variance":833.3333333333334};

// Run this file directly: npx tsx lessons/07-probability-distributions-and-simulation-basics/064-uniform-distribution-and-random-sampling.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
  console.log(JSON.stringify(lesson064(), null, 2));
}

Your output

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Expected output

{
  "samples": [
    100,
    125,
    150,
    175
  ],
  "mean": 150,
  "variance": 833.3333333333334
}

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Lesson notes

The rule

X = a + (b−a)U