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It is clear that the Theory of Probability works primarily with limited measures on measurable spaces.

On the other hand there is a folklore that says that what distinguishes Measure Theory and Probability Theory is the conditional probability and conditional expectation.

But conditional probabilities and conditional expectations are derived from Radon–Nikodym theorem and a measure with respect to another measure. The adon–Nikodym theorem is a typical result of Measure Theory.

Question 1: So we could see the Theory of Probability as a subdiscipline of Measure Theory?

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Question 2: It would be possible to give another basis for the Theory of Probability than not the Theory of Measure?

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Measure theory allows unbounded measures. There exists a uniform measure on the integers, for example, but there is no uniform probability. –  Thomas Andrews Mar 9 '12 at 14:06
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There is a discussion of what distinguishes probability theory from measure theory in section 10.2 of Loève's book Probability Theory I. –  t.b. Mar 9 '12 at 15:03
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Saying that probability is a subdiscipline of measure theory is like saying that mathematics is subdisciple of logic. –  Dirk Mar 9 '12 at 16:40
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Measure theory is a tool people use, probability theory is a field of mathematics. –  ShawnD Mar 9 '12 at 16:56
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"Probability theory is measure theory with soul". –  Leandro Mar 9 '12 at 17:00

1 Answer 1

up vote 10 down vote accepted

I would say that conditioning and independence is something that disctinct, expectation is used a lot in the measure theory as well, by the name of the Lebesgue integral.

The point is that the probability as a science before was maybe even more closer to physics than to math by being based on experiments. It became the classical Probability Theory (PT) when it was axiomatized the first half of XX century by the means of Measure Theory (MT). So MT is clearly a mathematical basis for the classical PT and in that sense you can consider PT to be a subdiscipline of MT.

There are two moments to mention, though.

  1. There is an algebraic approach to probability which starts with algebras of random variables and defines a linear functional on such algebras - which is an expectation. Shall we say that the Probability Theory is a subdiscipline of Abstract Algebra?

  2. In both cases - you start with something empirical: probability, random variables etc. You wish them to satisfy some kind of properties and by this you bring a particular structure: either a measure-theoretical, or an algebraic. However, there is an additional meaning of the results that you obtain. For example, the Law of Large Numbers and Central Limit Theorem are obtained by using pure measure-theoretical methods. By these results are very important exactly for Probability Theory. The interpretation of MT via probabilistic ideas provides you additional intuition about "how should it be" and help to understand "what does it mean".

That is completely an opinion which I've chosen for myself. Hope that it helps.

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That's really interesting. @Ilya have you got any good reference about this algebraic presentation of Probability Theory? –  Giorgio Mossa Mar 9 '12 at 14:25
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@ineff: I didn't work with it - just read this note in Terence Tao's blog. That's quite enough to have a first impression –  Ilya Mar 9 '12 at 14:30
    
@Ilya. His answer helped. I upvote your answer. But it leads me to another question. It is possible to base the theory of probability for some other branch of mathematics than the Theory of Measure? I think we can see the theory of probability as a science in existence outside of mathematics. But underlying it through the scientific method would be very redundant. After the scientific method has as one of its foundations and the probability inference. –  Elias Mar 9 '12 at 14:37
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@Elias: doesn't Abstract Algebra example work for you? –  Ilya Mar 9 '12 at 15:01
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@Elias: Have you checked out my link for ineff: for Terence Tao blog post? I'll put it better in my answer as well. There are some insights you may find interesting –  Ilya Mar 9 '12 at 16:10

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