Poisson Distribution and Event Counts

Install and import#

bash

npm install fintech-algorithms

import { poissonDistributionAndEventCounts } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/probability-distributions-and-simulation-basics/poisson-distribution-and-event-counts";

Signature#

poissonDistributionAndEventCounts(input)

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#

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#

poissonDistributionAndEventCounts(input)

Returns#

object with 2 fields: pmf, cdf

{
  "pmf": 0.2706705664732254,
  "cdf": 0.6766764161830635
}

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.

Read the article →

References#

Probability Distributions — NIST/SEMATECH e-Handbook
Probability Distributions — SciPy User Guide
Random Sampling — NumPy Documentation
Historical-example decision