Gaussian probability space

A Gaussian probability space is called irreducible if ${\displaystyle {\mathcal {F}}={\mathcal {F}}_{\mathcal {H}}}$. Such spaces are denoted as ${\displaystyle (\Omega ,{\mathcal {F}},P,{\mathcal {H}})}$. Non-irreducible spaces are used to work on subspaces or to extend a given probability space.[3] Irreducible Gaussian probability spaces are classified by the dimension of the Gaussian space ${\displaystyle {\mathcal {H}}}$.[4]

A subspace ${\displaystyle (\Omega ,{\mathcal {F}},P,{\mathcal {H}}_{1},{\mathcal {A}}_{{\mathcal {H}}_{1}}^{\perp })}$ of a Gaussian probability space ${\displaystyle (\Omega ,{\mathcal {F}},P,{\mathcal {H}},{\mathcal {F}}_{\mathcal {H}}^{\perp })}$ consists of

• a closed subspace ${\displaystyle {\mathcal {H}}_{1}\subset {\mathcal {H}}}$,
• a sub σ-algebra ${\displaystyle {\mathcal {A}}_{{\mathcal {H}}_{1}}^{\perp }\subset {\mathcal {F}}}$ of transverse random variables such that ${\displaystyle {\mathcal {A}}_{{\mathcal {H}}_{1}}^{\perp }}$ and ${\displaystyle {\mathcal {A}}_{{\mathcal {H}}_{1}}}$ are independent, ${\displaystyle {\mathcal {A}}={\mathcal {A}}_{{\mathcal {H}}_{1}}\otimes {\mathcal {A}}_{{\mathcal {H}}_{1}}^{\perp }}$ and ${\displaystyle {\mathcal {A}}\cap {\mathcal {F}}_{\mathcal {H}}^{\perp }={\mathcal {A}}_{{\mathcal {H}}_{1}}^{\perp }}$.[3]

Example:

Let ${\displaystyle (\Omega ,{\mathcal {F}},P,{\mathcal {H}},{\mathcal {F}}_{\mathcal {H}}^{\perp })}$ be a Gaussian probability space with a closed subspace ${\displaystyle {\mathcal {H}}_{1}\subset {\mathcal {H}}}$. Let ${\displaystyle V}$ be the orthogonal complement of ${\displaystyle {\mathcal {H}}_{1}}$ in ${\displaystyle {\mathcal {H}}}$. Since orthogonality implies independence between ${\displaystyle V}$ and ${\displaystyle {\mathcal {H}}_{1}}$, we have that ${\displaystyle {\mathcal {A}}_{V}}$ is independent of ${\displaystyle {\mathcal {A}}_{{\mathcal {H}}_{1}}}$. Define ${\displaystyle {\mathcal {A}}_{{\mathcal {H}}_{1}}^{\perp }}$ via ${\displaystyle {\mathcal {A}}_{{\mathcal {H}}_{1}}^{\perp }:=\sigma ({\mathcal {A}}_{V},{\mathcal {F}}_{\mathcal {H}}^{\perp })={\mathcal {A}}_{V}\vee {\mathcal {F}}_{\mathcal {H}}^{\perp }}$.

For ${\displaystyle G=L^{2}(\Omega ,{\mathcal {F}}_{\mathcal {H}}^{\perp },P)}$ we have ${\displaystyle L^{2}(\Omega ,{\mathcal {F}},P)=L^{2}((\Omega ,{\mathcal {F}}_{\mathcal {H}},P);G)}$.

Given a Gaussian probability space ${\displaystyle (\Omega ,{\mathcal {F}},P,{\mathcal {H}},{\mathcal {F}}_{\mathcal {H}}^{\perp })}$ one defines the algebra of cylindrical random variables

${\displaystyle \mathbb {A} _{\mathcal {H}}=\{F=P(X_{1},\dots ,X_{n}):X_{i}\in {\mathcal {H}}\}}$

where ${\displaystyle P}$ is a polynomial in ${\displaystyle \mathbb {R} [X_{n},\dots ,X_{n}]}$ and calls ${\displaystyle \mathbb {A} _{\mathcal {H}}}$ the fundamental algebra. For any ${\displaystyle p<\infty }$ it is true that ${\displaystyle \mathbb {A} _{\mathcal {H}}\subset L^{p}(\Omega ,{\mathcal {F}},P)}$.

For an irreducible Gaussian probability ${\displaystyle (\Omega ,{\mathcal {F}},P,{\mathcal {H}})}$ the fundamental algebra ${\displaystyle \mathbb {A} _{\mathcal {H}}}$ is a dense set in ${\displaystyle L^{p}(\Omega ,{\mathcal {F}},P)}$ for all ${\displaystyle p\in [1,\infty [}$.[4]

An irreducible Gaussian probability ${\displaystyle (\Omega ,{\mathcal {F}},P,{\mathcal {H}})}$ where a basis was chosen for ${\displaystyle {\mathcal {H}}}$ is called a numerical model. Two numerical models are isomorphic if their Gaussian spaces have the same dimension.[4]

Given a separable Hilbert space ${\displaystyle {\mathcal {G}}}$, there exists always a canoncial irreducible Gaussian probability space ${\displaystyle \operatorname {Seg} ({\mathcal {G}})}$ called the Segal model with ${\displaystyle {\mathcal {G}}}$ as a Gaussian space.[5]