Let $X=R^n$ and $Y=R^m$ be two Euclidean spaces with $m<n$. Let $\varphi$ and $\phi$ are two smooth maps from $X$ to $Y$, and $\mu$ a probability measure on $X$. Is there any relationship between $(\varphi+\phi)_\#\mu$ with $\varphi_\#\mu$ and $\phi_\#\mu$ (where $\#$ denotes the push-forward of a measure)?
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6$\begingroup$ What kind of statement are you hoping for? Obviously, lots of things can happen. For example, if $\varphi=-\psi$, then $(\varphi+\psi)_*\mu$ is the Dirac measure, but the individual image measures can be anything. $\endgroup$– Christian RemlingCommented Apr 15, 2015 at 22:13
1 Answer
The problem is one of linear programming. To see this more clearly, let us assume for a moment that $Y$ is a finite set (rather than $\mathbb{R}^m$). Let me write $\mu f^{-1}$ instead of $f_\#\mu$. Then the problem is whether, for given probability measures $\mu_\varphi$, $\mu_\phi$, and $\mu_{\varphi+\phi}$ over $Y$ (which are "candidates" for $\mu\varphi^{-1}$, $\mu\phi^{-1}$, and $\mu(\varphi+\phi)^{-1}$, respectively), there exist nonnegative real numbers $p(u,v)$ (which are "candidates" for the values of $\mu(\{x\in X\colon\varphi(x)=u,\phi(x)=v\})$ for $(u,v)\in Y\times Y$) such that $\sum_{v\in Y}p(y,v)=\mu_\varphi(\{y\})$ and $\sum_{u\in Y}p(u,y)=\mu_\phi(\{y\})$ for all $y\in Y$, with the additional (affine) restrictions that $\sum\{p(u,v)\colon u+v=y,u\in Y,v\in Y\}=\mu_{\varphi+\phi}(y)$ for all $y\in Y$. Going back to $Y=\mathbb{R}^m$, one sees that the problem is one of infinite-dimensional linear programming.
Relevant here is the fundamental paper "The Existence of Probability Measures with Given Marginals" by Strassen (1965) in The Annals of Mathematical Statistics deals with the existence of a probability measure $\mu$ on a product space $X\times Y$ given the marginals $\mu\pi_X^{-1}$ and $\mu\pi_Y^{-1}$ (where $\pi_X$ and $\pi_Y$ are the projections from $X\times Y$ to $X$ and $Y$, respectively) plus further affine restrictions on $\mu$, as is the case here.
One simple necessary condition on the probability measures $\mu_\varphi$, $\mu_\phi$, and $\mu_{\varphi+\phi}$ is, obviously, that $$\int_Y y\mu_{\varphi+\phi}(dy)=\int_Y y\mu_{\varphi}(dy)+\int_Y y\mu_{\phi}(dy),$$ provided that (say) at least two of these three integrals exist and are finite.