|
|
Line 1: |
Line 1: |
− | The [[Probability distribution|probability distribution]] of a [[Brownian motion|Brownian motion]] <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100601.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100602.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100603.png" />, where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100604.png" /> is a [[Probability space|probability space]]. The Wiener measure is denoted by <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100605.png" /> or <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100606.png" />. Brownian motion <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100607.png" /> is a [[Gaussian process|Gaussian process]] such that
| + | <!-- |
| + | w1100601.png |
| + | $#A+1 = 54 n = 0 |
| + | $#C+1 = 54 : ~/encyclopedia/old_files/data/W110/W.1100060 Wiener measure |
| + | Automatically converted into TeX, above some diagnostics. |
| + | Please remove this comment and the {{TEX|auto}} line below, |
| + | if TeX found to be correct. |
| + | --> |
| | | |
− | <table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100608.png" /></td> </tr></table>
| + | {{TEX|auto}} |
| + | {{TEX|done}} |
| | | |
− | Given a Brownian motion <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w1100609.png" />, one can form a new Brownian motion <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006010.png" /> satisfying:
| + | The [[Probability distribution|probability distribution]] of a [[Brownian motion|Brownian motion]] $ B ( t, \omega ) $, |
| + | $ t \geq 0 $, |
| + | $ \omega \in \Omega $, |
| + | where $ ( \Omega, {\mathcal B}, {\mathsf P} ) $ |
| + | is a [[Probability space|probability space]]. The Wiener measure is denoted by $ m $ |
| + | or $ \mu ^ {W} $. |
| + | Brownian motion $ B $ |
| + | is a [[Gaussian process|Gaussian process]] such that |
| | | |
− | i) <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006011.png" /> is continuous in <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006012.png" /> for almost all <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006013.png" />.
| + | $$ |
| + | {\mathsf E} ( B ( t ) ) \equiv 0, \quad {\mathsf E} ( B ( t ) \cdot B ( s ) ) = \min ( t, s ) . |
| + | $$ |
| | | |
− | ii) <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006014.png" /> for every <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006015.png" />.
| + | Given a Brownian motion $ B ( t, \omega ) $, |
| + | one can form a new Brownian motion $ {\overline{B}\; } ( t, \omega ) $ |
| + | satisfying: |
| | | |
− | Such a process <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006016.png" /> is called a continuous version of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006017.png" />.
| + | i) $ {\overline{B}\; } ( t, \omega ) $ |
| + | is continuous in $ t $ |
| + | for almost all $ \omega $. |
| | | |
− | The Kolmogorov–Prokhorov theorem tells that the probability distribution <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006018.png" /> of the Brownian motion <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006019.png" /> can be introduced in the space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006020.png" /> of all continuous functions on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006021.png" />.
| + | ii) $ {\mathsf P} ( {\overline{B}\; } ( t, \omega ) = B ( t, \omega ) ) = 1 $ |
| + | for every $ t $. |
| | | |
− | Let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006022.png" /> be the topological Borel field (cf. also [[Borel field of sets|Borel field of sets]]) of subsets of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006023.png" />. The [[Measure space|measure space]] <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006024.png" /> thus obtained is the Wiener measure space.
| + | Such a process $ {\overline{B}\; } ( t, \omega ) $ |
| + | is called a continuous version of $ B ( t, \omega ) $. |
| | | |
− | The integral of a <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006025.png" />-measurable functional on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006026.png" /> with respect to <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006027.png" /> is defined in the usual manner. (See also [[Stochastic integral|Stochastic integral]].) | + | The Kolmogorov–Prokhorov theorem tells that the probability distribution $ m $ |
| + | of the Brownian motion $ B ( t ) $ |
| + | can be introduced in the space $ C = C [ 0, \infty ) $ |
| + | of all continuous functions on $ [ 0, \infty ) $. |
| | | |
− | An elementary and important example of a <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006028.png" />-measurable functional of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006029.png" /> is a stochastic bilinear form, given by <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006030.png" />, where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006031.png" /> is an <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006032.png" />-function. It is usually denoted by <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006033.png" />. It is, in fact, defined by <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006034.png" /> for smooth functions <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006035.png" />. For a general <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006036.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006037.png" /> can be approximated by stochastic bilinear forms defined by smooth functions <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006038.png" />. An integral of this type is called a Wiener integral. Under certain restrictions, such as non-anticipation, the integral can be extended to the case where the integrand is a functional of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006039.png" /> and <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006040.png" />. And an even more general case has been proposed.
| + | Let $ {\mathcal B} $ |
| + | be the topological Borel field (cf. also [[Borel field of sets|Borel field of sets]]) of subsets of $ C $. |
| + | The [[Measure space|measure space]] $ ( C, {\mathcal B}, m ) $ |
| + | thus obtained is the Wiener measure space. |
| | | |
− | The class of general (non-linear) functionals of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006041.png" /> is introduced as follows. Let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006042.png" /> be the [[Hilbert space|Hilbert space]] of all complex-valued, square-<img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006043.png" />-integrable functionals on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006044.png" />. Then, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006045.png" /> admits a direct sum decomposition ([[Fock space|Fock space]]) | + | The integral of a $ {\mathcal B} $- |
| + | measurable functional on $ C $ |
| + | with respect to $ m $ |
| + | is defined in the usual manner. (See also [[Stochastic integral|Stochastic integral]].) |
| | | |
− | <table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006046.png" /></td> </tr></table>
| + | An elementary and important example of a $ {\mathcal B} $- |
| + | measurable functional of $ y \in C $ |
| + | is a stochastic bilinear form, given by $ \langle { {\dot{y} } , f } \rangle $, |
| + | where $ f $ |
| + | is an $ L _ {2} [ 0, \infty ) $- |
| + | function. It is usually denoted by $ f ( y ) $. |
| + | It is, in fact, defined by $ - \int _ {0} ^ \infty {y ( t ) {\dot{f} } ( t ) } {dt } $ |
| + | for smooth functions $ f $. |
| + | For a general $ f $, |
| + | $ f ( y ) $ |
| + | can be approximated by stochastic bilinear forms defined by smooth functions $ f $. |
| + | An integral of this type is called a Wiener integral. Under certain restrictions, such as non-anticipation, the integral can be extended to the case where the integrand is a functional of $ t $ |
| + | and $ y $. |
| + | And an even more general case has been proposed. |
| | | |
− | The subspace <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006047.png" /> is spanned by the Fourier–Hermite polynomials of degree <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006048.png" />, which are of the form | + | The class of general (non-linear) functionals of $ y $ |
| + | is introduced as follows. Let $ H $ |
| + | be the [[Hilbert space|Hilbert space]] of all complex-valued, square- $ m $- |
| + | integrable functionals on $ C $. |
| + | Then, $ H $ |
| + | admits a direct sum decomposition ([[Fock space|Fock space]]) |
| | | |
− | <table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006049.png" /></td> </tr></table>
| + | $$ |
| + | H = \oplus _ { n } {\mathcal H} _ {n} . |
| + | $$ |
| | | |
− | where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006050.png" /> and <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006051.png" /> is a complete [[Orthonormal system|orthonormal system]] in the Hilbert space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006052.png" />. The space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006053.png" /> can be interpreted as the space of multiple Wiener integrals of degree <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w110/w110060/w11006054.png" />, due to K. Itô. | + | The subspace $ {\mathcal H} _ {n} $ |
| + | is spanned by the Fourier–Hermite polynomials of degree $ n $, |
| + | which are of the form |
| + | |
| + | $$ |
| + | \prod _ { j } H _ {n _ {j} } \left ( { |
| + | \frac{\left \langle {y,f _ {j} } \right \rangle }{\sqrt 2 } |
| + | } \right ) , |
| + | $$ |
| + | |
| + | where $ \Sigma n _ {j} = n $ |
| + | and $ \{ f _ {j} \} $ |
| + | is a complete [[Orthonormal system|orthonormal system]] in the Hilbert space $ L _ {2} [ 0, \infty ) $. |
| + | The space $ H _ {n} $ |
| + | can be interpreted as the space of multiple Wiener integrals of degree $ n $, |
| + | due to K. Itô. |
| | | |
| ====References==== | | ====References==== |
| <table><TR><TD valign="top">[a1]</TD> <TD valign="top"> R. Cameron, W.T. Martin, "The orthogonal development of non-linear functionals in series of Fourier–Hermite functionals" ''Ann. of Math. (2)'' , '''48''' pp. 385–392</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top"> T. Hida, "Brownian motion" , ''Applications of Mathematics'' , '''11''' , Springer (1980)</TD></TR></table> | | <table><TR><TD valign="top">[a1]</TD> <TD valign="top"> R. Cameron, W.T. Martin, "The orthogonal development of non-linear functionals in series of Fourier–Hermite functionals" ''Ann. of Math. (2)'' , '''48''' pp. 385–392</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top"> T. Hida, "Brownian motion" , ''Applications of Mathematics'' , '''11''' , Springer (1980)</TD></TR></table> |
The probability distribution of a Brownian motion $ B ( t, \omega ) $,
$ t \geq 0 $,
$ \omega \in \Omega $,
where $ ( \Omega, {\mathcal B}, {\mathsf P} ) $
is a probability space. The Wiener measure is denoted by $ m $
or $ \mu ^ {W} $.
Brownian motion $ B $
is a Gaussian process such that
$$
{\mathsf E} ( B ( t ) ) \equiv 0, \quad {\mathsf E} ( B ( t ) \cdot B ( s ) ) = \min ( t, s ) .
$$
Given a Brownian motion $ B ( t, \omega ) $,
one can form a new Brownian motion $ {\overline{B}\; } ( t, \omega ) $
satisfying:
i) $ {\overline{B}\; } ( t, \omega ) $
is continuous in $ t $
for almost all $ \omega $.
ii) $ {\mathsf P} ( {\overline{B}\; } ( t, \omega ) = B ( t, \omega ) ) = 1 $
for every $ t $.
Such a process $ {\overline{B}\; } ( t, \omega ) $
is called a continuous version of $ B ( t, \omega ) $.
The Kolmogorov–Prokhorov theorem tells that the probability distribution $ m $
of the Brownian motion $ B ( t ) $
can be introduced in the space $ C = C [ 0, \infty ) $
of all continuous functions on $ [ 0, \infty ) $.
Let $ {\mathcal B} $
be the topological Borel field (cf. also Borel field of sets) of subsets of $ C $.
The measure space $ ( C, {\mathcal B}, m ) $
thus obtained is the Wiener measure space.
The integral of a $ {\mathcal B} $-
measurable functional on $ C $
with respect to $ m $
is defined in the usual manner. (See also Stochastic integral.)
An elementary and important example of a $ {\mathcal B} $-
measurable functional of $ y \in C $
is a stochastic bilinear form, given by $ \langle { {\dot{y} } , f } \rangle $,
where $ f $
is an $ L _ {2} [ 0, \infty ) $-
function. It is usually denoted by $ f ( y ) $.
It is, in fact, defined by $ - \int _ {0} ^ \infty {y ( t ) {\dot{f} } ( t ) } {dt } $
for smooth functions $ f $.
For a general $ f $,
$ f ( y ) $
can be approximated by stochastic bilinear forms defined by smooth functions $ f $.
An integral of this type is called a Wiener integral. Under certain restrictions, such as non-anticipation, the integral can be extended to the case where the integrand is a functional of $ t $
and $ y $.
And an even more general case has been proposed.
The class of general (non-linear) functionals of $ y $
is introduced as follows. Let $ H $
be the Hilbert space of all complex-valued, square- $ m $-
integrable functionals on $ C $.
Then, $ H $
admits a direct sum decomposition (Fock space)
$$
H = \oplus _ { n } {\mathcal H} _ {n} .
$$
The subspace $ {\mathcal H} _ {n} $
is spanned by the Fourier–Hermite polynomials of degree $ n $,
which are of the form
$$
\prod _ { j } H _ {n _ {j} } \left ( {
\frac{\left \langle {y,f _ {j} } \right \rangle }{\sqrt 2 }
} \right ) ,
$$
where $ \Sigma n _ {j} = n $
and $ \{ f _ {j} \} $
is a complete orthonormal system in the Hilbert space $ L _ {2} [ 0, \infty ) $.
The space $ H _ {n} $
can be interpreted as the space of multiple Wiener integrals of degree $ n $,
due to K. Itô.
References
[a1] | R. Cameron, W.T. Martin, "The orthogonal development of non-linear functionals in series of Fourier–Hermite functionals" Ann. of Math. (2) , 48 pp. 385–392 |
[a2] | T. Hida, "Brownian motion" , Applications of Mathematics , 11 , Springer (1980) |