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Unformatted text preview: Notes on random variables, density functions, and measures September 29, 2010 1 Probability density functions and probabil ity measures We defined a probability density function (pdf for short) as a function f : R → [0 , ∞ ) satisfying integraldisplay R f ( x ) dx = 1 . (Of course, this means that f must be Lebesgueintegrable, and hence mea surable.) Likewise, we defined a “probability mass function”, which I will also refer to as a pdf, as a function p : { a 1 , a 2 , a 3 , ... } → [0 , 1] satisfying summationdisplay i p ( a i ) = 1 . So, mass functions are analogues of density functions for discrete (finite, or possibly countably infinite) sets of real numbers. What a pdf gives us is a probability measure for subsets of the real num bers. Specifically, suppose A ∈ B ( R ). Then, we can define P ( A ) := integraldisplay R 1 A ( x ) f ( x ) dx = integraldisplay A f ( x ) dx. (1) And we can do the same in the discrete case, for if A ⊆ { a 1 , a 2 , ... } , then we can define P ( A ) := summationdisplay a i ∈ A p ( a i ) . 1 There is a way to unify both of these types of measures: Basically, let μ : B ( R ) → [0 , ∞ ). Then, for A ∈ B ( R ) we use the notation integraldisplay R 1 A ( x ) dμ ( x ) , or , alternatively...
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This note was uploaded on 10/23/2011 for the course MATH 3225 taught by Professor Staff during the Spring '08 term at Georgia Tech.
 Spring '08
 Staff
 Statistics, Probability

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