Probability and Random Variables
10 algorithms in Financial Mathematics, Statistics, and Data Foundations · 10 with asserted arithmetic.
In this family#
-
Experiments, Outcomes, Sample Spaces, and Events verified
experimentsOutcomesSampleSpacesAndEvents(input) -
Probability Rules, Complements, Unions, and Intersections verified
probabilityRulesComplementsUnionsAndIntersections(input) -
Conditional Probability verified
conditionalProbability(input) -
Independence and Dependence verified
independenceAndDependence(input) -
Bayes' Theorem and Base Rates verified
bayesTheoremAndBaseRates(input) -
Discrete and Continuous Random Variables verified
discreteAndContinuousRandomVariables(input) -
Expected Value verified
expectedValue(input) -
Variance, Moments, and Moment-Generating Intuition verified
varianceMomentsAndMomentGeneratingIntuition(input) -
Joint, Marginal, and Conditional Distributions verified
jointMarginalAndConditionalDistributions(input) -
Covariance and Correlation of Random Variables verified
covarianceAndCorrelationOfRandomVariables(input)
What they share#
Every topic here is a record-transform, so once you have
called one the rest follow the same shape. Import paths differ only in the final segment:
import { experimentsOutcomesSampleSpacesAndEvents } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/probability-and-random-variables/experiments-outcomes-sample-spaces-and-events";
import { probabilityRulesComplementsUnionsAndIntersections } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/probability-and-random-variables/probability-rules-complements-unions-and-intersections";Read them in the order above — the sequence is pedagogical, not alphabetical.
Where this sits#
Financial Mathematics, Statistics, and Data Foundations collects 120 algorithms across 12 families. For the concept behind this family rather than the call signatures, see the concept guides.