imported>Ro Thorpe |
imported>Jitse Niesen |
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| This is an example of the [[strong law of large numbers]]. | | This is an example of the [[strong law of large numbers]]. |
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| ==References==
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| #P. Billingsley, ''Probability and Measure'' (2 ed.), ser. Wiley Series in Probability and Mathematical Statistics, Wiley, 1986.
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| #D. Williams, ''Probability with Martingales'', Cambridge : Cambridge University Press, 1991.
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| #E. Wong and B. Hajek, ''Stochastic Processes in Engineering Systems'', New York: Springer-Verlag, 1985.
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| ==See also==
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| *[[Stochastic convergence]]
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| *[[Convergence in distribution]]
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| *[[Convergence in probability]]
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| *[[Convergence in r-th order mean]]
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| ==Related topics==
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| *[[Stochastic variable]]
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| *[[Stochastic process]]
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| *[[Stochastic diffential equation]]
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| <!--==External links==-->
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Revision as of 05:53, 14 July 2008
Almost sure convergence is one of the four main modes of stochastic convergence. It may be viewed as a notion of convergence for random variables that is similar to, but not the same as, the notion of pointwise convergence for real functions.
Definition
In this section, a formal definition of almost sure convergence will be given for complex vector-valued random variables, but it should be noted that a more general definition can also be given for random variables that take on values on more abstract topological spaces. To this end, let be a probability space (in particular, ) is a measurable space). A (-valued) random variable is defined to be any measurable function , where is the sigma algebra of Borel sets of . A formal definition of almost sure convergence can be stated as follows:
A sequence of random variables is said to converge almost surely to a random variable if for all , where is some measurable set satisfying . An equivalent definition is that the sequence converges almost surely to if for all , where is some measurable set with . This convergence is often expressed as:
or
.
Important cases of almost sure convergence
If we flip a coin n times and record the percentage of times it comes up heads, the result will almost surely approach 50% as .
This is an example of the strong law of large numbers.