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'''Measure algebra''' may refer to:
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<ref> [http://hea-www.harvard.edu/AstroStat http://hea-www.harvard.edu/AstroStat]; <nowiki> http://www.incagroup.org </nowiki>; <nowiki> http://astrostatistics.psu.edu </nowiki> </ref>
  
* algebra of measures on a topological group with the operation of convolution; see [[measure algebra (harmonic analysis)]];
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====Notes====
* normed Boolean algebra, either in general or consisting of equivalence classes of measurable sets; see [[measure algebra (measure theory)]].
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<references />
  
=Measure algebra (measure theory)=
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-------------------------------------------
  
{{MSC.|28Axx|28A50,60A10}}
 
  
[[:Category:Classical measure theory]]
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{|
 
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| A || B || C
{{TEX|done}}
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|-
 
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| X || Y || Z
$\newcommand{\Om}{\Omega}
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|}
\newcommand{\om}{\omega}
 
\newcommand{\F}{\mathcal F}
 
\newcommand{\B}{\mathcal B}
 
\newcommand{\M}{\mathcal M} $
 
A '''measure algebra''' is a pair $(B,\mu)$ where $B$ is a Boolean σ-algebra and $\mu$ is a (strictly) positive measure on $B$. However, about the greatest value $\mu(\bsone_B)$ of $\mu$, assumptions differ from $\mu(\bsone_B)=1$ (that is, $\mu$ is a probability measure) in {{Cite|Ha2|p. 43}} and {{Cite|K|Sect. 17.F}} to $\mu(\bsone_B)<\infty$ (that is, $\mu$ is a totally finite measure) in {{Cite|G|Sect. 2.1}} to $\mu(\bsone_B)\le\infty$ in {{Cite|P|Sect. 1.4C}} and {{Cite|Ha1|Sect. 40}}.
 
 
 
 
 
-------------------------------
 
''Also: Lebesgue-Rokhlin space''
 
 
 
 
 
A [[probability space]] is called '''standard''' if it satisfies the following equivalent conditions:
 
* it is [[Measure space#Isomorphism|almost isomorphic]] to the real line with some [[probability distribution]] (in other words, a [[Measure space#Completion|completed]] [[Borel measure|Borel]] [[probability measure]], that is, a [[Lebesgue–Stieltjes integral|Lebesgue–Stieltjes]] probability measure);
 
* it is a [[standard Borel space]] endowed with a [[probability measure]], completed, and possibly augmented with a [[Measure space#null|null set]];
 
* it is [[Measure space#Completion|complete]], [[Measure space#Perfect and standard|perfect]], and the [[Hilbert space#L2 space|corresponding Hilbert space]] is separable.
 
 
 
====The isomorphism theorem====
 
 
 
Every standard probability space consists of an [[Measure space#Atoms and continuity|atomic]] (discrete) part and an atomless (continuous) part (each part may be empty). The discrete part is finite or countable; here, all subsets are  measurable, and the probability of each subset is the sum of probabilities of its elements.
 
 
 
'''Theorem 1.''' All atomless standard probability spaces are mutually almost isomorphic.
 
 
 
That  is, up to almost isomorphism we have "the" atomless standard probability space. Its "incarnations" include the spaces $\R^n$ with atomless probability distributions (be they [[Continuous distribution|absolutely continuous]], [[Singular distribution|singular]] or mixed), as well as the set of all continuous functions $[0,\infty)\to\R$ with the [[Wiener measure]]. That is instructive: topological notions such as dimension do not apply to probability spaces.
 
  
====Measure preserving maps====
 
  
The inverse to a bijective [[Measure space#measure preserving|measure preserving]] map is measure preserving provided that it is measurable; in this (not general) case the given map is a [[Measure space#Isomorphism|strict isomorphism]]. Here is an important fact in two equivalent forms.
 
  
'''Theorem 2a.''' Every bijective measure preserving map between standard probability spaces is a strict isomorphism.
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-----------------------------------------
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'''Theorem 2b.''' If $(\Om,\F,P)$ is a standard probability space and $\F_1\subset\F$ a sub-σ-field such that $(\Om,\F_1,P|_{\F_1})$ is also standard then $\F_1=\F$.
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$\newcommand*{\longhookrightarrow}{\lhook\joinrel\relbar\joinrel\rightarrow}$
  
Recall a topological fact similar to Theorem 2: if a bijective map  between compact Hausdorff topological spaces is continuous then it is a homeomorphism. Moreover, if a Hausdorff topology is  weaker than a compact topology then these two topologies are equal, which has the following measure-theory counterpart stronger than Theorem 2 (in two equivalent forms).
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<asy>
Here we call a probability space ''countably separated'' if its underlying measurable space is [[Measurable space#separated|countably separated]].
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size(100,100);
 +
label(scale(1.7)*'$T(\\Sigma)\hookrightarrow T(\\Sigma,X)$',(0,0));
 +
</asy>
  
'''Theorem 3a.''' Every bijective measure preserving map from a standard probability space to a  countably separated complete probability space  is a strict isomorphism.
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<asy>
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size(220,220);
  
'''Theorem 3b.''' If $(\Om,\F,P)$ is a standard probability space and $\F_1\subset\F$ is a countably separated sub-σ-field then $(\Om,\F,P)$ is the completion of $(\Om,\F_1,P|_{\F_1})$.
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import math;
  
A continuous image of a compact topological space is always a compact set. In contrast, the image of a measurable set under a (non-bijective) measure-preserving map need not be measurable (indeed, the image of a null set need not be null; try the projection $\R^2\to\R^1$). Nevertheless, Theorem 4 (below) is a partial measure-theory counterpart, stronger than Theorem 3.
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int kmax=40;
  
'''Theorem 4.''' Let $(\Om,\F,P)$ be a standard probability space, $(\Om_1,\F_1,P_1)$ a countably separated complete probability space, and $f:\Om\to\Om_1$ a measure preserving map. Then $(\Om_1,\F_1,P_1)$ is also standard, and $A_1\in\F_1\iff A\in\F$ whenever $A_1\subset\Om_1$ and $A=f^{-1}(A_1)$. In particular, the image $f(\Om)$belongs to $\F_1$. (See {{Cite|Ru|Th. 3-2}} and {{Cite|H|Prop. 9}}.)
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guide g;
 +
for (int k=-kmax; k<=kmax; ++k) {
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  real phi = 0.2*k*pi;
 +
  real rho = 1;
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  if (k!=0) {
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    rho = sin(phi)/phi;
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  }
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  pair z=rho*expi(phi);
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  g=g..z;
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}
 +
 
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draw (g);
  
====References====
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defaultpen(0.75);
 +
draw ( (0,0)--(1.3,0), dotted, Arrow(SimpleHead,5) );
 +
dot ( (1,0) );
 +
label ( "$a$", (1,0), NE );
  
{|
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</asy>
|valign="top"|{{Ref|P}}||  Karl Petersen, "Ergodic theory", Cambridge (1983). &nbsp; {{MR|0833286}} &nbsp; {{ZBL|0507.28010}}
 
|-
 
|valign="top"|{{Ref|H1}}|| P.R. Halmos, "Measure theory", Van Nostrand (1950). &nbsp; {{MR|0033869}} &nbsp; {{ZBL|0040.16802}}
 
|-
 
|valign="top"|{{Ref|H2}}|| P.R. Halmos, "Lectures on ergodic theory", Math. Soc. Japan (1956). &nbsp; {{MR|0097489}} &nbsp; {{ZBL|0073.09302}}
 
|-
 
|valign="top"|{{Ref|G}}|| Eli Glasner, "Ergodic theory via joinings", Amer. Math. Soc. (2003). &nbsp; {{MR|1958753}} &nbsp; {{ZBL|1038.37002}}
 
|-
 
|valign="top"|{{Ref|K}}|| Alexander  S.  Kechris, "Classical    descriptive set theory", Springer-Verlag  (1995). &nbsp;  {{MR|1321597}} &nbsp; {{ZBL|0819.04002}}
 
|-
 
|valign="top"|{{Ref|Ru}}|| Thierry de la Rue, "Espaces de Lebesgue", ''Séminaire de Probabilités XXVII,'' Lecture Notes in Mathematics, 1557 (1993), Springer, Berlin, pp. 15–21. &nbsp;  {{MR|1308547}} &nbsp; {{ZBL|0788.60001}}
 
|-
 
|valign="top"|{{Ref|H}}||  Jean Haezendonck, "Abstract  Lebesgue-Rohlin  spaces",  ''Bull. Soc.  Math. de Belgique'' '''25'''  (1973), 243–258.  &nbsp;  {{MR|0335733}} &nbsp;  {{ZBL|0308.60006}}
 
|-
 
|valign="top"|{{Ref|HN}}|| P.R. Halmos, J. von Neumann, "Operator methods in classical mechanics, II", ''Annals of Mathematics (2)'' '''43''' (1942), 332–350.  &nbsp;  {{MR|0006617}} &nbsp;  {{ZBL|0063.01888}}
 
|-
 
|valign="top"|{{Ref|Ro}}|| V.A. Rokhlin, (1962), "On the fundamental ideas of measure theory", ''Translations (American Mathematical Society) Series 1,'' 10 (1962), 1–54. &nbsp; {{MR|0047744}} &nbsp; Translated from Russian: Рохлин, В. А. (1949), "Об основных понятиях теории меры", Математический Сборник (Новая Серия) 25(67): 107–150. &nbsp; {{MR|0030584}}
 
|-
 
|valign="top"|{{Ref|F}}||  D.H. Fremlin, "Measure theory", Torres  Fremlin, Colchester. Vol. 1:  2004 &nbsp; {{MR|2462519}} &nbsp;  {{ZBL|1162.28001}}; Vol. 2:  2003  &nbsp; {{MR|2462280}}  &nbsp; {{ZBL|1165.28001}}; Vol. 3:  2004 &nbsp;  {{MR|2459668}}  &nbsp; {{ZBL|1165.28002}}; Vol. 4:  2006 &nbsp;    {{MR|2462372}} &nbsp; {{ZBL|1166.28001}}
 
|}
 

Latest revision as of 07:12, 13 March 2016

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Notes

  1. http://hea-www.harvard.edu/AstroStat; http://www.incagroup.org ; http://astrostatistics.psu.edu


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$\newcommand*{\longhookrightarrow}{\lhook\joinrel\relbar\joinrel\rightarrow}$

How to Cite This Entry:
Boris Tsirelson/sandbox1. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Boris_Tsirelson/sandbox1&oldid=21683