I am studying probability theory in infinite dimensional spaces and want to know if things that hold in the usual(?) theory holds in a similar manner.
Fix a real separable Hilbert space $H$ with inner product $\langle\cdot,\cdot\rangle$. Let $X$ be a $H$-valued Gaussian random variable on a probability space$(\Omega,\mathscr{F},P)$ such that $E\langle X,h\rangle\langle X,h'\rangle=\langle Qh,h'\rangle$ (e.g. a $Q$-Wiener process at a fixed time) with a trace class covariance operator $Q\colon H\to H$.
The usual probability theory tells us that for a nice function $f\colon\mathbb{R}\to\mathbb{R}$ we have that $f(\langle X,h\rangle)$ and $f(\langle X,h'\rangle)$ are independent. Also that if $f$ is linear (or affine) $f(\langle X,h\rangle)$ is again Gaussian, but not in general, e.g., $f(\cdot)=|\cdot|$.
My question is,
For nice $T\colon H\to H$ not necessarily linear, can we hope $ \langle TX,h\rangle $ and $ \langle TX,h'\rangle $ are independent, or uncorrelated? When $h,h'$ are eigenfunctions of $Q$ such that they are orthogonal, and $T$ is linear diagonalized by $(h)$, then it should be true, but I have no idea how to prove or disprove the above in general. I wanted to go back to the good old definition of independence of random variables (independence of $\sigma$-algebra generated by them), but didn't seem it would help.