PMF, PDF, CDF, Survival, and Quantile Functions
Install and import#
npm install fintech-algorithmsimport { pmfPdfCdfSurvivalAndQuantileFunctions } from "fintech-algorithms/foundations/probability-distributions-and-simulation-basics/pmf-pdf-cdf-survival-and-quantile-functions";Signature#
pmfPdfCdfSurvivalAndQuantileFunctions(input)Reads an empirical sample from input.values and reports where the threshold input.x sits inside it, plus the sample quantile at probability input.p.
Parameters#
| Name | Type | Notes |
|---|---|---|
input | D00Input | One record. values is the observed sample, x is the threshold to evaluate the empirical CDF and survival at, and p is the quantile probability. Other keys in the record are ignored.values: non-empty list of finite numbers · p: between 0 and 1 inclusive |
Returns#
D00Output
An object with cdf (share of values at or below x), survival (share strictly above x) and quantile (the linearly interpolated sample quantile at p).
Errors#
- When p is missing or outside 0 to 1 — this check runs for every topic in the family — throws RangeError
- When values is missing, empty, or contains a non-finite number — throws RangeError
Complexity: time O(n log n),
space O(n).
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#
pmfPdfCdfSurvivalAndQuantileFunctions(input)Returns#
object with 2 fields: cdf, survival
{
"cdf": 0.8,
"survival": 0.2
}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