Question: Is there an intuitive way to think about mean square convergence of functions?
(mean square convergence = limit in quadratic mean = limit in $L^2$)
An equivalent concern is an intuitive way to think about functions which are "close" in $L^2$ to the constant zero function. (Since $X_n \to X$ in $L^2$ implies that $X_n - X \to 0$ in $L^2$.)
I want a simple way to explain to people that the mode of convergence used in the definitions of stochastic integrals is meaningful, ideally by appealing somehow to geometric or physical intuition.
In other words, I want to be able to think of or draw a picture which suggests a simple analogy relating convergence in mean square to something people might be more familiar with -- maybe waves or surfaces with elliptic curvature, or something like this.
Background: Both pointwise and uniform convergence of functions are easy to visualize as curves in the plane or space getting closer to each other.
However, seemingly unless we are talking about holomorphic functions, mean square convergence does not necessarily imply either of the above modes of convergence, as is often pointed in introductory discussions of Fourier series.
Is there some way to leverage one of the following notions to make mean square convergence seem like an intuitively appealing and meaningful concept, rather than a last-resort backup when we can't get pointwise or uniform convergence to work?
- spectral decomposition
- Karhunen-Loeve decomposition
- variance, covariance
- square integrable functions represent finite energy in quantum mechanics
- orthogonality/orthogonal decompositions
- weak stationarity
- elliptic operators
- quadratic curves and/or surfaces
and so on. This question is clearly related: Why consider square-integrable functions?
For example, what sort of everyday phenomenon should pictures like the following evoke?