Namespaces
Variants
Actions

Difference between revisions of "Wiener measure"

From Encyclopedia of Mathematics
Jump to: navigation, search
(Importing text file)
 
m (tex encoded by computer)
 
Line 1: Line 1:
The [[Probability measure|probability measure]] <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979301.png" /> on the space <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979302.png" /> of continuous real-valued functions <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979303.png" /> on the interval <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979304.png" />, defined as follows. Let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979305.png" /> be an arbitrary sample of points from <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979306.png" /> and let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979307.png" /> be Borel sets on the real line. Let <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979308.png" /> denote the set of functions <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w0979309.png" /> for which <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w09793010.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w09793011.png" />. Then
+
<!--
 +
w0979301.png
 +
$#A+1 = 20 n = 0
 +
$#C+1 = 20 : ~/encyclopedia/old_files/data/W097/W.0907930 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/w097/w097930/w09793012.png" /></td> <td valign="top" style="width:5%;text-align:right;">(*)</td></tr></table>
+
{{TEX|auto}}
 +
{{TEX|done}}
  
<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/w097/w097930/w09793013.png" /></td> </tr></table>
+
The [[Probability measure|probability measure]]  $  \mu _ {W} $
 +
on the space  $  C[ 0, 1] $
 +
of continuous real-valued functions  $  x $
 +
on the interval  $  [ 0, 1] $,
 +
defined as follows. Let  $  0 < t _ {1} < \dots < t _ {n} \leq  1 $
 +
be an arbitrary sample of points from  $  [ 0, 1] $
 +
and let  $  A _ {1} \dots A _ {n} $
 +
be Borel sets on the real line. Let  $  C( t _ {1} \dots t _ {n} ; A _ {1} \dots A _ {n} ) $
 +
denote the set of functions  $  x \in C[ 0, 1] $
 +
for which  $  x( t _ {k} ) \in A _ {k} $,
 +
$  k = 1 \dots n $.  
 +
Then
  
<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/w097/w097930/w09793014.png" /></td> </tr></table>
+
$$ \tag{* }
 +
\mu _ {W} ( C ( t _ {1} \dots t _ {n} ; A _ {1} \dots A _ {n} )) =
 +
$$
  
where
+
$$
 +
= \
 +
\int\limits _ { A _ 1 } p ( t _ {1} , x _ {1} )  dx _ {1}  \int\limits _ { A _ 2 } p ( t _ {2} - t _ {1} , x _ {2} - x _ {1} )  dx _ {2} \dots
 +
$$
  
<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/w097/w097930/w09793015.png" /></td> </tr></table>
+
$$
 +
{} \dots \int\limits _ { A _ n } p ( t _ {n} - t _ {n-} 1 , x _ {n} - x _ {n-} 1 )  dx _ {n} ,
 +
$$
  
Using the theorem on the extension of a measure it is possible to define the value of the measure <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w09793016.png" /> on all Borel sets of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w09793017.png" /> on the basis of equation (*).
+
where
  
 +
$$
 +
p ( t, x)  =  {
 +
\frac{1}{\sqrt {2 \pi t } }
 +
} e ^ {- x  ^ {2} / 2 t } .
 +
$$
  
 +
Using the theorem on the extension of a measure it is possible to define the value of the measure  $  \mu _ {W} $
 +
on all Borel sets of  $  C[ 0, 1] $
 +
on the basis of equation (*).
  
 
====Comments====
 
====Comments====
The Wiener measure was introduced by N. Wiener [[#References|[a1]]] in 1923; it was the first major extension of integration theory beyond a finite-dimensional setting. The construction outlined above extends easily to define Wiener measure <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w09793018.png" /> on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w09793019.png" />. The coordinate process <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/w/w097/w097930/w09793020.png" /> is then known as [[Brownian motion|Brownian motion]] or the [[Wiener process|Wiener process]]. Its formal derivative  "dxt/dt"  is known as Gaussian [[White noise|white noise]].
+
The Wiener measure was introduced by N. Wiener [[#References|[a1]]] in 1923; it was the first major extension of integration theory beyond a finite-dimensional setting. The construction outlined above extends easily to define Wiener measure $  \mu _ {W} $
 +
on $  C [ 0, \infty ) $.  
 +
The coordinate process $  x( t) $
 +
is then known as [[Brownian motion|Brownian motion]] or the [[Wiener process|Wiener process]]. Its formal derivative  "dxt/dt"  is known as Gaussian [[White noise|white noise]].
  
 
====References====
 
====References====
 
<table><TR><TD valign="top">[a1]</TD> <TD valign="top">  N. Wiener,  "Differential space"  ''J. Math. &amp; Phys.'' , '''2'''  (1923)  pp. 132–174</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top">  T. Hida,  "Brownian motion" , Springer  (1980)</TD></TR><TR><TD valign="top">[a3]</TD> <TD valign="top">  I. Karatzas,  S.E. Shreve,  "Brownian motion and stochastic calculus" , Springer  (1988)</TD></TR><TR><TD valign="top">[a4]</TD> <TD valign="top">  L. Partzsch,  "Vorlesungen zum eindimensionalen Wienerschen Prozess" , Teubner  (1984)</TD></TR><TR><TD valign="top">[a5]</TD> <TD valign="top">  J. Yeh,  "Stochastic processes and the Wiener integral" , M. Dekker  (1973)</TD></TR><TR><TD valign="top">[a6]</TD> <TD valign="top">  S. Albeverio,  J.E. Fenstad,  R. Høegh-Krohn,  T. Lindstrøm,  "Nonstandard methods in stochastic analysis and mathematical physics" , Acad. Press  (1986)</TD></TR></table>
 
<table><TR><TD valign="top">[a1]</TD> <TD valign="top">  N. Wiener,  "Differential space"  ''J. Math. &amp; Phys.'' , '''2'''  (1923)  pp. 132–174</TD></TR><TR><TD valign="top">[a2]</TD> <TD valign="top">  T. Hida,  "Brownian motion" , Springer  (1980)</TD></TR><TR><TD valign="top">[a3]</TD> <TD valign="top">  I. Karatzas,  S.E. Shreve,  "Brownian motion and stochastic calculus" , Springer  (1988)</TD></TR><TR><TD valign="top">[a4]</TD> <TD valign="top">  L. Partzsch,  "Vorlesungen zum eindimensionalen Wienerschen Prozess" , Teubner  (1984)</TD></TR><TR><TD valign="top">[a5]</TD> <TD valign="top">  J. Yeh,  "Stochastic processes and the Wiener integral" , M. Dekker  (1973)</TD></TR><TR><TD valign="top">[a6]</TD> <TD valign="top">  S. Albeverio,  J.E. Fenstad,  R. Høegh-Krohn,  T. Lindstrøm,  "Nonstandard methods in stochastic analysis and mathematical physics" , Acad. Press  (1986)</TD></TR></table>

Latest revision as of 08:29, 6 June 2020


The probability measure $ \mu _ {W} $ on the space $ C[ 0, 1] $ of continuous real-valued functions $ x $ on the interval $ [ 0, 1] $, defined as follows. Let $ 0 < t _ {1} < \dots < t _ {n} \leq 1 $ be an arbitrary sample of points from $ [ 0, 1] $ and let $ A _ {1} \dots A _ {n} $ be Borel sets on the real line. Let $ C( t _ {1} \dots t _ {n} ; A _ {1} \dots A _ {n} ) $ denote the set of functions $ x \in C[ 0, 1] $ for which $ x( t _ {k} ) \in A _ {k} $, $ k = 1 \dots n $. Then

$$ \tag{* } \mu _ {W} ( C ( t _ {1} \dots t _ {n} ; A _ {1} \dots A _ {n} )) = $$

$$ = \ \int\limits _ { A _ 1 } p ( t _ {1} , x _ {1} ) dx _ {1} \int\limits _ { A _ 2 } p ( t _ {2} - t _ {1} , x _ {2} - x _ {1} ) dx _ {2} \dots $$

$$ {} \dots \int\limits _ { A _ n } p ( t _ {n} - t _ {n-} 1 , x _ {n} - x _ {n-} 1 ) dx _ {n} , $$

where

$$ p ( t, x) = { \frac{1}{\sqrt {2 \pi t } } } e ^ {- x ^ {2} / 2 t } . $$

Using the theorem on the extension of a measure it is possible to define the value of the measure $ \mu _ {W} $ on all Borel sets of $ C[ 0, 1] $ on the basis of equation (*).

Comments

The Wiener measure was introduced by N. Wiener [a1] in 1923; it was the first major extension of integration theory beyond a finite-dimensional setting. The construction outlined above extends easily to define Wiener measure $ \mu _ {W} $ on $ C [ 0, \infty ) $. The coordinate process $ x( t) $ is then known as Brownian motion or the Wiener process. Its formal derivative "dxt/dt" is known as Gaussian white noise.

References

[a1] N. Wiener, "Differential space" J. Math. & Phys. , 2 (1923) pp. 132–174
[a2] T. Hida, "Brownian motion" , Springer (1980)
[a3] I. Karatzas, S.E. Shreve, "Brownian motion and stochastic calculus" , Springer (1988)
[a4] L. Partzsch, "Vorlesungen zum eindimensionalen Wienerschen Prozess" , Teubner (1984)
[a5] J. Yeh, "Stochastic processes and the Wiener integral" , M. Dekker (1973)
[a6] S. Albeverio, J.E. Fenstad, R. Høegh-Krohn, T. Lindstrøm, "Nonstandard methods in stochastic analysis and mathematical physics" , Acad. Press (1986)
How to Cite This Entry:
Wiener measure. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Wiener_measure&oldid=49220
This article was adapted from an original article by A.V. Skorokhod (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article