Normal Distribution and Standard Normal
Install and import#
npm install fintech-algorithmsimport { normalDistributionAndStandardNormal } from "fintech-algorithms/foundations/probability-distributions-and-simulation-basics/normal-distribution-and-standard-normal";Signature#
normalDistributionAndStandardNormal(input)Evaluates the normal density and cumulative probability at input.x for a distribution with mean mu and standard deviation sigma, and returns the standardised score that maps x onto the standard normal.
Parameters#
| Name | Type | Notes |
|---|---|---|
input | D00Input | One record carrying x (the point to evaluate), mu and sigma (the distribution's mean and standard deviation), plus p, which the family entry point validates before dispatching.sigma: strictly positive · p: between 0 and 1 inclusive |
Returns#
D00Output
An object with pdf (the density at x), cdf (the cumulative probability at x, computed from an Abramowitz-Stegun error function approximation rather than an exact integral) and z (the standardised score).
Errors#
- When p is missing or outside 0 to 1 — this check runs for every topic in the family — throws RangeError
- When sigma is zero or negative — this check runs for every topic from A05 onward — throws RangeError
Complexity: time O(1),
space O(1).
Worked example#
verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.
Input#
{
"values": [0.2, 0.5, 0.7, 1, 1.4],
"x": 1,
"p": 0.3,
"n": 5,
"k": 2,
"lambda": 2,
"seed": 42,
"sampleCount": 8,
"mu": 0,
"sigma": 1,
"df": 5,
"shape": 2,
"scale": 1.5,
"components": [
{
"weight": 0.7,
"mean": 0,
"sd": 1
},
{
"weight": 0.3,
"mean": 3,
"sd": 0.8
}
]
}Call#
normalDistributionAndStandardNormal(input)Returns#
object with 2 fields: pdf, cdf
{
"pdf": 0.24197072451914337,
"cdf": 0.8413447460685428
}Diagrams#
How it works#
This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.
References#
- Probability Distributions — NIST/SEMATECH e-Handbook
- Probability Distributions — SciPy User Guide
- Random Sampling — NumPy Documentation
- Historical-example decision