Uniform polytope

Template:More footnotes In mathematics, the disintegration theorem is a result in measure theory and probability theory. It rigorously defines the idea of a non-trivial "restriction" of a measure to a measure zero subset of the measure space in question. It is related to the existence of conditional probability measures. In a sense, "disintegration" is the opposite process to the construction of a product measure.

Motivation

Consider the unit square in the Euclidean plane R², S = [0, 1] × [0, 1]. Consider the probability measure μ defined on S by the restriction of two-dimensional Lebesgue measure λ² to S. That is, the probability of an event E ⊆ S is simply the area of E. We assume E is a measurable subset of S.

Consider a one-dimensional subset of S such as the line segment L_x = {x} × [0, 1]. L_x has μ-measure zero; every subset of L_x is a μ-null set; since the Lebesgue measure space is a complete measure space,

$E\subseteq L_x⟹\mu (E)=0.$

While true, this is somewhat unsatisfying. It would be nice to say that μ "restricted to" L_x is the one-dimensional Lebesgue measure λ¹, rather than the zero measure. The probability of a "two-dimensional" event E could then be obtained as an integral of the one-dimensional probabilities of the vertical "slices" E ∩ L_x: more formally, if μ_x denotes one-dimensional Lebesgue measure on L_x, then

$\mu (E)=\underset{[0,1]}{\int }{\mu }_x(E\cap L_x)\mathrm{d}x$

for any "nice" E ⊆ S. The disintegration theorem makes this argument rigorous in the context of measures on metric spaces.

Statement of the theorem

(Hereafter, P(X) will denote the collection of Borel probability measures on a metric space (X, d).)

Let Y and X be two Radon spaces (i.e. separable metric spaces on which every probability measure is a Radon measure). Let μ ∈ P(Y), let π : Y → X be a Borel-measurable function, and let ν ∈ P(X) be the pushforward measure ν = π_∗(μ) = μ ∘ π⁻¹. Then there exists a ν-almost everywhere uniquely determined family of probability measures {μ_x}_x∈X ⊆ P(Y) such that

• the function $x\mapsto {\mu }_x$ is Borel measurable, in the sense that $x\mapsto {\mu }_x(B)$ is a Borel-measurable function for each Borel-measurable set B ⊆ Y;
• μ_x "lives on" the fiber π⁻¹(x): for ν-almost all x ∈ X,

${\mu }_x(Y\setminus {\pi }^{-1}(x))=0,$

and so μ_x(E) = μ_x(E ∩ π⁻¹(x));

• for every Borel-measurable function f : Y → [0, ∞],

$\underset{Y}{\int }f(y)\mathrm{d}\mu (y)=\underset{X}{\int }\underset{{\pi }^{-1}(x)}{\int }f(y)\mathrm{d}{\mu }_x(y)\mathrm{d}\nu (x).$

In particular, for any event E ⊆ Y, taking f to be the indicator function of E,

$\mu (E)=\underset{X}{\int }{\mu }_x\left(E\right)\mathrm{d}\nu (x).$

Applications

Product spaces

The original example was a special case of the problem of product spaces, to which the disintegration theorem applies.

When Y is written as a Cartesian product Y = X₁ × X₂ and π_i : Y → X_i is the natural projection, then each fibre π₁⁻¹(x₁) can be canonically identified with X₂ and there exists a Borel family of probability measures $\{{\mu }_{x_1}{\}}_{x_1\in X_1}$ in P(X₂) (which is (π₁)_∗(μ)-almost everywhere uniquely determined) such that

$\mu =\underset{X_1}{\int }{\mu }_{x_1}\mu ({\pi }_1^{-1}(\mathrm{d}{x}_1))=\underset{X_1}{\int }{\mu }_{x_1}\mathrm{d}({\pi }_1{)}_{*}(\mu )(x_1),$

which is in particular

$\underset{X_1\times X_2}{\int }f(x_1,x_2)\mu (\mathrm{d}{x}_1,\mathrm{d}{x}_2)=\underset{X_1}{\int }(\underset{X_2}{\int }f(x_1,x_2)\mu (\mathrm{d}{x}_2|x_1))\mu ({\pi }_1^{-1}(\mathrm{d}{x}_1))$

and

$\mu (A\times B)=\underset{A}{\int }\mu \left(B\right|x_1)\mu ({\pi }_1^{-1}(\mathrm{d}{x}_1)).$

The relation to conditional expectation is given by the identities

$\mathrm{E}(f|{\pi }_1)(x_1)=\underset{X_2}{\int }f(x_1,x_2)\mu (\mathrm{d}{x}_2|x_1),$

$\mu (A\times B|{\pi }_1)(x_1)=1_A(x_1)\cdot \mu (B|x_1).$

Vector calculus

The disintegration theorem can also be seen as justifying the use of a "restricted" measure in vector calculus. For instance, in Stokes' theorem as applied to a vector field flowing through a compact surface Σ ⊂ R³, it is implicit that the "correct" measure on Σ is the disintegration of three-dimensional Lebesgue measure λ³ on Σ, and that the disintegration of this measure on ∂Σ is the same as the disintegration of λ³ on ∂Σ.

Conditional distributions

The disintegration theorem can be applied to give a rigorous treatment of conditioning probability distributions in statistics, while avoiding purely abstract formulations of conditional probability.

See also

• Joint probability distribution
• Copula (statistics)
• Conditional expectation

References

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