Pushforward measure

In

Given measurable spaces ${\displaystyle (X_{1},\Sigma _{1})}$ and ${\displaystyle (X_{2},\Sigma _{2})}$, a measurable mapping ${\displaystyle f\colon X_{1}\to X_{2}}$ and a measure ${\displaystyle \mu \colon \Sigma _{1}\to [0,+\infty ]}$, the pushforward of ${\displaystyle \mu }$ is defined to be the measure ${\displaystyle f_{*}(\mu )\colon \Sigma _{2}\to [0,+\infty ]}$ given by

${\displaystyle f_{*}(\mu )(B)=\mu \left(f^{-1}(B)\right)}$ for ${\displaystyle B\in \Sigma _{2}.}$

This definition applies mutatis mutandis for a signed or complex measure. The pushforward measure is also denoted as ${\displaystyle \mu \circ f^{-1}}$, ${\displaystyle f_{\sharp }\mu }$, ${\displaystyle f\sharp \mu }$, or ${\displaystyle f\#\mu }$.

Main property: change-of-variables formula

Theorem:^[1] A measurable function g on X₂ is integrable with respect to the pushforward measure f_∗(μ) if and only if the composition ${\displaystyle g\circ f}$ is integrable with respect to the measure μ. In that case, the integrals coincide, i.e.,

${\displaystyle \int _{X_{2}}g\,d(f_{*}\mu )=\int _{X_{1}}g\circ f\,d\mu .}$

Note that in the previous formula ${\displaystyle X_{1}=f^{-1}(X_{2})}$.

Examples and applications

• A natural "
  real line R. Let λ also denote the restriction of Lebesgue measure to the interval [0, 2π) and let f : [0, 2π) → S¹ be the natural bijection defined by f(t) = exp(i t). The natural "Lebesgue measure" on S¹ is then the push-forward measure f_∗(λ). The measure f_∗(λ) might also be called "arc length
  measure" or "angle measure", since the f_∗(λ)-measure of an arc in S¹ is precisely its arc length (or, equivalently, the angle that it subtends at the centre of the circle.)
• The previous example extends nicely to give a natural "Lebesgue measure" on the n-dimensional torus Tⁿ. The previous example is a special case, since S¹ = T¹. This Lebesgue measure on Tⁿ is, up to normalization, the Haar measure for the compact, connected Lie group Tⁿ.
• continuous dual space
  to X is a Gaussian measure on R.
• Consider a measurable function f : X → X and the composition of f with itself n times:

${\displaystyle f^{(n)}=\underbrace {f\circ f\circ \dots \circ f} _{n\mathrm {\,times} }:X\to X.}$

This iterated function forms a dynamical system. It is often of interest in the study of such systems to find a measure μ on X that the map f leaves unchanged, a so-called invariant measure, i.e one for which f_∗(μ) = μ.

• One can also consider quasi-invariant measures for such a dynamical system: a measure ${\displaystyle \mu }$ on ${\displaystyle (X,\Sigma )}$ is called quasi-invariant under ${\displaystyle f}$ if the push-forward of ${\displaystyle \mu }$ by ${\displaystyle f}$ is merely
  equivalent
  to the original measure μ, not necessarily equal to it. A pair of measures ${\displaystyle \mu ,\nu }$ on the same space are equivalent if and only if ${\displaystyle \forall A\in \Sigma :\ \mu (A)=0\iff \nu (A)=0}$, so ${\displaystyle \mu }$ is quasi-invariant under ${\displaystyle f}$ if ${\displaystyle \forall A\in \Sigma :\ \mu (A)=0\iff f_{*}\mu (A)=\mu {\big (}f^{-1}(A){\big )}=0}$

• Many natural probability distributions, such as the chi distribution, can be obtained via this construction.

• Random variables induce pushforward measures. They map a probability space into a codomain space and endow that space with a probability measure defined by the pushforward. Furthermore, because random variables are functions (and hence total functions), the inverse image of the whole codomain is the whole domain, and the measure of the whole domain is 1, so the measure of the whole codomain is 1. This means that random variables can be composed ad infinitum and they will always remain as random variables and endow the codomain spaces with probability measures.

A generalization

In general, any

The adjoint to the push-forward is the

See also

• Measure-preserving dynamical system
• Normalizing flow
• Optimal transport

Notes

References

• Bogachev, Vladimir I. (2007), Measure Theory, Berlin:
  ISBN 9783540345138
• Teschl, Gerald (2015), Topics in Real and Functional Analysis