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Unformatted text preview: STAT 430/510 Lecture 16 STAT 430/510 Probability Hui Nie Lecture 16 June 24th, 2009 STAT 430/510 Lecture 16 Review Sum of Independent Normal Random Variables Sum of Independent Poisson Random Variables Sum of Independent Binomial Random Variables Conditional Distributions: Discrete Case STAT 430/510 Lecture 16 Conditional Distributions: Continuous Case Let X and Y be jointly continuous r.v.’s. Then for any x value for which f X ( x ) > 0, the conditional pdf of Y given X = x is f Y  X ( y  x ) = f ( x , y ) f X ( x ) , ∞ < y < ∞ For any set A , P ( Y ∈ A  X = x ) = Z A f Y  X ( y  x ) dy = Z A f ( x , y ) f X ( x ) dy If X and Y are independent, then f Y  X ( y  x ) = f ( x , y ) f X ( x ) = f X ( x ) f Y ( y ) f X ( x ) = f Y ( y ) STAT 430/510 Lecture 16 Example The joint density of X and Y is given by f ( x , y ) = 12 5 x ( 2 x y ) , < x < 1 , < y < 1 , otherwise Compute the conditional density of X given that Y = y , where 0 < y < 1....
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This note was uploaded on 06/28/2009 for the course STAT 430 taught by Professor Krieger during the Summer '08 term at UPenn.
 Summer '08
 KRIEGER
 Binomial, Probability

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