Finite joint distribution
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Value tuples of random variables on one common finite probability space, together with exact masses, specify a finite joint distribution.
mko-finite-joint-distribution · definition · v0.10.0
Mathematical statement
A two-dimensional finite joint distribution lists distinct pairs (x,y) and nonnegative exact masses summing exactly to 1. Marginals, rectangle events and single-pair masses all aggregate from that same joint law.
Representative formula (exact TeX)
J(X,Y)
Assumptions
- The listed support consists of distinct integer pairs and the masses are exact rational numbers.
- The joint law is a mathematical model, not a claim of empirical data fitting or a continuous density.
Symbol scope
J— finite joint distributionX— first random variableY— second random variable
Intuition, definition, and misconceptions
The two marginals aggregate the same listed support separately by coordinate.
A rectangle event requires both coordinates to belong to their specified sets.
Marginals generally do not determine the complete joint law.
Do not treat two marginal tables as a joint distribution or allow masses that sum only approximately to 1.
Adopted derivation guide
Finite joint distribution: validating a joint mass table and aggregating marginals and events from one joint lawThe original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.
Prerequisites
- Finite discrete probability distribution — Joint value pairs form a finite support and probability mass function.
- Real random variables on finite sample spaces — Coordinate values come from variables on a common sample space.
- Probability on finite sample spaces — Event masses follow the probability axioms.
- Sample space — A common probability space grounds the joint semantics.
Verification boundary
Only complete finite joint PMFs are handled; no continuous-distribution conclusion is inferred.
not_formalized
This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.
Next obligation
Add covariance, independence of random variables and conditional expectation.
Same-source structured data
This page and the interactive view use the same object and existing language edition; this adds no proof or human review.
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