Talk:Stochastic convergence: Difference between revisions

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imported>Robert Tito
imported>Greg Woodhouse
(Almost sure convergence - response)
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The definition in the text doesn't make sense. In general, something is true almost surely (a.s.) if it is true with a probability of 1. It is almost surely true that a randomly chosen number is not 4. [[User:Greg Woodhouse|Greg Woodhouse]] 12:08, 28 June 2007 (CDT)
The definition in the text doesn't make sense. In general, something is true almost surely (a.s.) if it is true with a probability of 1. It is almost surely true that a randomly chosen number is not 4. [[User:Greg Woodhouse|Greg Woodhouse]] 12:08, 28 June 2007 (CDT)
:Greg, some would argue if the probability has a chance of being not true ≤0.01‰ the convergenge to a value is true, and the tail can be forgotten. Compare to series. [[User:Robert Tito|Robert Tito]]&nbsp;|&nbsp;<span style="background:grey">&nbsp;<font color="yellow"><b>[[User talk:Robert Tito|Talk]]</b></font>&nbsp;</span> 12:44, 28 June 2007 (CDT)
:Greg, some would argue if the probability has a chance of being not true ≤0.01‰ the convergenge to a value is true, and the tail can be forgotten. Compare to series. [[User:Robert Tito|Robert Tito]]&nbsp;|&nbsp;<span style="background:grey">&nbsp;<font color="yellow"><b>[[User talk:Robert Tito|Talk]]</b></font>&nbsp;</span> 12:44, 28 June 2007 (CDT)
I understand. It's a technical concept from measure theory. If two functions (say ''f'' and ''g'') are equal except on a set of measure 0, we say ''f = g'' almost everywhere. This is important because their (Lebesgue) integrals over a given set will always be equal and it is convenient to ''identify'' such functions because then if we define
:<math>(f,g) = \int_A f\,\bar{g}\,dx</math>
defines a metric on <math>\scriptstyle L^2(A)</math>, giving it the structure of a metric space. If we didn't identify functions that wee equal almost everywhere (i.e., treat them as the same function), the purported metric would not be positive definite.
On a foundational level, probability theory is essentially measure theory, (and distributions are just measurable functions that, when integrated ovderf a set ''A'' give the probability of ''A''). It is just a convention that probability theorists use the phrase "almost surely" instead of "almost everywhere", the meanings are the same. Of course, I'm almost sure :) you already know this! [[User:Greg Woodhouse|Greg Woodhouse]] 13:28, 28 June 2007 (CDT)

Revision as of 12:28, 28 June 2007


Article Checklist for "Stochastic convergence"
Workgroup category or categories Mathematics Workgroup, Physics Workgroup, Chemistry Workgroup [Categories OK]
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Underlinked article? Not specified
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Checklist last edited by Ragnar Schroder 11:13, 28 June 2007 (CDT)

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Work in progress

This page is a work in progress, I'm struggling with the latex and some other stuff, there may be actual errors now.

Ragnar Schroder 12:04, 28 June 2007 (CDT)

Almost sure convergence

The definition in the text doesn't make sense. In general, something is true almost surely (a.s.) if it is true with a probability of 1. It is almost surely true that a randomly chosen number is not 4. Greg Woodhouse 12:08, 28 June 2007 (CDT)

Greg, some would argue if the probability has a chance of being not true ≤0.01‰ the convergenge to a value is true, and the tail can be forgotten. Compare to series. Robert Tito |  Talk  12:44, 28 June 2007 (CDT)

I understand. It's a technical concept from measure theory. If two functions (say f and g) are equal except on a set of measure 0, we say f = g almost everywhere. This is important because their (Lebesgue) integrals over a given set will always be equal and it is convenient to identify such functions because then if we define

defines a metric on , giving it the structure of a metric space. If we didn't identify functions that wee equal almost everywhere (i.e., treat them as the same function), the purported metric would not be positive definite.

On a foundational level, probability theory is essentially measure theory, (and distributions are just measurable functions that, when integrated ovderf a set A give the probability of A). It is just a convention that probability theorists use the phrase "almost surely" instead of "almost everywhere", the meanings are the same. Of course, I'm almost sure :) you already know this! Greg Woodhouse 13:28, 28 June 2007 (CDT)