# Confused on general approach to Stochastic Process theory.

I am studying Stochastic processes and I am a bit confused on the general approach to their study.

In engineering textbooks a concept called "Mean Squared" Calculus is used to define things like time integration/differentiation/continuity of a Stochastic Process.

How does this interact with Ito integration? It seems Ito integration is focused (loosely speaking) on integration with respect to Brownian motion or some other stochastic process, not time integration.

In addition I haven't found any information on Mean-Squared calculus outside of engineering texts, the Stochastic Processes book(math) I have read doesn't even mention it.

So what's going on? Is Mean-Squared Calculus a mathematically recognized theory? Why don't math-books focused on Stochastic Processes use it?

• Mean-square is synonymous with $L^2$. Is that what you are asking about? Commented Apr 18, 2018 at 4:58
• I think so, limits are taken with respect to the expectation of the square, so I assume so. Most of the theory is built upon the convergence of Cauchy Sequences(Hilbert Space) which is taken for granted. Commented Apr 18, 2018 at 9:06
• Yes, that can be taken for granted because every Hilbert space is a complete metric space. Since $L^2$ convergence plays an extensive role in the theory of stochastic integration, you might be able to map the statements found in the engineering literature to the more precise mathematical statements with a bit of work. Commented Apr 18, 2018 at 13:00
• Have any book recommendations? For example, engineering books talk about Mean-Square(L^2) continuity, differentiation, integration WRT to time. I know of texts which focus on integration WRT a stochastic process(Ito) but not time integration. Commented Apr 19, 2018 at 5:33
• I suggest Protter, P. E. (2013). Stochastic Integration and Differential Equations. Springer. Time integration is treated as part of integration wrt. a finite variation process, one of the components of a semimartingale. The latter is in a sense the most general integrator for a stochastic integral. Commented Apr 19, 2018 at 6:02

• You need a bit more than $L^2$ continuity to get the existence of a continuous modification. I think $L^2$ continuity plus some growth condition on the covariance function should be sufficient. Please have a look at en.wikipedia.org/wiki/Kolmogorov_continuity_theorem Commented May 18, 2018 at 21:41