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Difference between revisions of "Probability measure"

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====References====
 
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<table><TR><TD valign="top">[1]</TD> <TD valign="top"> A.N. Kolmogorov,   "Foundations of the theory of probability" , Chelsea, reprint (1950) (Translated from Russian)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top"> B.V. Gnedenko,   "The theory of probability" , Chelsea, reprint (1962) (Translated from Russian)</TD></TR></table>
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<table><TR><TD valign="top">[1]</TD> <TD valign="top"> A.N. Kolmogorov, "Foundations of the theory of probability" , Chelsea, reprint (1950) (Translated from Russian) {{MR|0032961}} {{ZBL|}} </TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top"> B.V. Gnedenko, "The theory of probability" , Chelsea, reprint (1962) (Translated from Russian) {{MR|0149513}} {{ZBL|0102.34402}} </TD></TR></table>
  
  
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<table><TR><TD valign="top">[a1]</TD> <TD valign="top"> P. Billingsley,   "Probability and measure" , Wiley (1979)</TD></TR></table>
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<table><TR><TD valign="top">[a1]</TD> <TD valign="top"> P. Billingsley, "Probability and measure" , Wiley (1979) {{MR|0534323}} {{ZBL|0411.60001}} </TD></TR></table>

Revision as of 10:31, 27 March 2012

probability distribution, probability

A real non-negative function on a class of subsets (events) of a non-empty set (the space of elementary events) forming a -field (i.e. a set closed with respect to countable set-theoretic operations) such that

if for (-additivity).

Examples of probability measures.

1) ; is the class of all subsets of ; (this probability measure corresponds to a random experiment consisting in throwing a symmetrical coin; if heads correspond to 1 while tails correspond to 2, the probability of throwing heads (tails) is 1/2);

2) ; is the class of all subsets of ;

where (the Poisson distribution);

3) ; is the class of Borel subsets of ;

(the normal distribution);

4) is the space of continuous real functions on that vanish at the point zero; is the class of Borel subsets with respect to the topology of uniform convergence; is the measure which is uniquely defined by the formula

where is an arbitrary natural number and (the Wiener measure).

References

[1] A.N. Kolmogorov, "Foundations of the theory of probability" , Chelsea, reprint (1950) (Translated from Russian) MR0032961
[2] B.V. Gnedenko, "The theory of probability" , Chelsea, reprint (1962) (Translated from Russian) MR0149513 Zbl 0102.34402


Comments

References

[a1] P. Billingsley, "Probability and measure" , Wiley (1979) MR0534323 Zbl 0411.60001
How to Cite This Entry:
Probability measure. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Probability_measure&oldid=23647
This article was adapted from an original article by V.V. Sazonov (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article