Math 2.0

Probability theory and stochastic processes Definition

Coupling

Builds on Probability measureMarginal distributionRandom variableProbability space

Definition

Let μ\mu be a probability measure on a measurable space (E,ℰ)(E, \mathcal{E}) and ν\nu a probability measure on a measurable space (F,ℱ)(F, \mathcal{F}). A coupling of μ\mu and ν\nu is a probability measure γ\gamma on the product space (E×F,ℰ⊗ℱ)(E \times F, \mathcal{E} \otimes \mathcal{F}) whose marginals are μ\mu and ν\nu: γ(A×F)=μ(A)andγ(E×B)=ν(B)for all A∈ℰ, B∈ℱ.\gamma(A \times F) = \mu(A) \quad \text{and} \quad \gamma(E \times B) = \nu(B) \qquad \text{for all } A \in \mathcal{E}, \ B \in \mathcal{F}. Equivalently, a coupling is a pair of random variables (X,Y)(X, Y) on one probability space with XX of law μ\mu and YY of law ν\nu. The joint law of such a pair is a coupling in the first sense.

Remarks

Example: the product measure μ⊗ν\mu \otimes \nu is a coupling, the independent coupling. Indeed (μ⊗ν)(A×F)=μ(A)ν(F)=μ(A)(\mu \otimes \nu)(A \times F) = \mu(A) \nu(F) = \mu(A), and in the same way for the second marginal. Example: for 0≤p≤q≤10 \le p \le q \le 1, let UU be uniform on [0,1][0, 1], X=𝟏{U<p}X = \mathbf{1}_{\set{U < p}} and Y=𝟏{U<q}Y = \mathbf{1}_{\set{U < q}}. Then XX and YY are Bernoulli with parameters pp and qq, and X≤YX \le Y everywhere. Non-example: on {0,1}2\set{0, 1}^2, the point mass δ(0,0)\delta_{(0, 0)} is not a coupling of two Bernoulli laws with parameter 12\frac{1}{2}, because its first marginal is δ0\delta_0. If E=FE = F and the diagonal is measurable, every coupling satisfies the coupling inequality dTV(μ,ν)≤P(X≠Y)d_{TV}(\mu, \nu) \le P(X \ne Y), where dTVd_{TV} is the total variation distance.

Reading path

To understand this page from the ground up, read these 5 pages in order. The foundations come first.

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Each page uses the pages above it. The colour of the bar on a box shows how strong the evidence is, and the box also gives it as text. The list has the same pages.
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  1. Marginal distribution concept
  2. Probability measure concept
  3. Probability space concept
  4. Random variable concept
  5. Coupling definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Coupling_(probability)
About this page
TagTD7S2HF19H
URIm2://TD7S2HF19H
Kinddefinition
WritingW0 stub
MSC60E05, 60E15
DefinesCoupling
UsesProbability measure, Marginal distribution, Random variable, Probability space
Changed2 November 2026

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