MHB Notations with Almost everywhere

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The symbol $dt \otimes dP$ refers to the product measure in the context of stochastic differential equations (SDEs), indicating that certain properties hold almost everywhere with respect to this measure. In the given setting, $X(t,\omega)$ belonging to $\mathcal{D}(A)$ $dt \otimes d\mathbb{P}$-a.e. means it holds true for all but a set of measure zero. The Lebesgue measure $dt$ is used alongside the probability measure $d\mathbb{P}$. For further understanding, consulting a Real Analysis textbook that covers product measures is recommended. The discussion emphasizes the importance of these measures in defining strong solutions to SDEs.
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Good day! I came across this symbol $dt \otimes dP$-a.e. in the book of Mandrekar (page 72) Stochastic Differential Equations in Infinite Dimensions: With Applications ... - Leszek Gawarecki, Vidyadhar Mandrekar - Google Books.

What does this symbol mean? I understand that in real analysis, given a measure space $(X,\mathcal{A},\mu)$ we say that a property holds $\mu$-a.e. if there is a set $N$ such that $\mu(N)=0$ and the property holds for all $x\in (X\smallsetminus N).$

I am a newbie with the symbols $dt\otimes dP$ since $dt$ and $dP$ aren't measures?
Also, can you suggest a book with detailed explanation on such notation?

Thanks a lot.
 
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I have not been able to see page 72 in google books. I am pretty sure that those symbols stand for the product measure though, and that by $dt$ they mean the Lebesgue measure.

You should be able to find a section on the product measure in most Real Analysis books (almost everywhere :D).
 
PaulRS said:
I have not been able to see page 72 in google books. I am pretty sure that those symbols stand for the product measure though, and that by $dt$ they mean the Lebesgue measure.

You should be able to find a section on the product measure in most Real Analysis books (almost everywhere :D).

I see. Below is taken from page 72 of the book. It is part of the definition of a strong solution of the semilinear SDE. This is the setting:

Let $K$ and $H$ be real separable Hilbert spaces, and $W_t$ be a $K$-valued $Q$-Wiener process on a complete filtered probability space $\Big(\Omega,\mathcal{F},\{ \mathcal{F}_t\}_{t\leq T},\mathbb{P}\Big)$ with the filtration $\mathcal{F}_t$ satisfying the usual conditions. We consider the semilinear SDEs on $[0,T]$ in $H$ in the general form
\begin{align*}
dX(t) &= (AX(t) +F(t,X))dt + B(t,X)dW_t\\
X(0) &= \xi_0.
\end{align*}
Here, $A: \mathcal{D}(A) \subset H \to H$ is the generator of a $C_0$-semigroup of operators $\{ S_t, t\geq 0\}$ on $H.$ The coefficients $F$ and $B$ are, in general, nonlinear mappings,
\begin{align*}
F&:\Omega\times [0,T] \times C\big([0,T],H\big) \to H\\
B&:\Omega\times [0,T] \times C\big([0,T],H\big) \to \mathcal{L}_{2}(K_Q,H).
\end{align*}
Finally, the initial condition $\xi_0$ is an $\mathcal{F}_0$-measurable $H$-valued random variable.

In the definition of a strong solution of the above SSDE, one requirement is the ff:

$X(t,\omega)\in\mathcal{D}(A)$ $dt\otimes d\mathbb{P}$-a.e.

Does this mean that $X(t,\omega)$ belongs to $\mathcal{D}(A)$, except for a set of measure zero? Which measure will we use? The product measure $Leb\otimes\mathbb{P}.$Thanks again for further enlightenment.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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