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Lecture_8

# Lecture_8 - 1 HIGHER DIMENSIONAL RANDOM VECTORS Lecture 8...

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1 HIGHER DIMENSIONAL RANDOM VECTORS Lecture 8 ORIE3500/5500 Summer2009 Chen Class Today Random Vectors with higher dimensions Independent Random Variables 1 Higher Dimensional Random Vectors It is the expected extension of bivariate random vectors to higher dimensions. ( X 1 , X 2 , . . . , X n ) is said to be jointly distributed if each X i is a random variable and they have a joint cumulative distribution function F X 1 , ··· ,X n ( x 1 , . . . , x n ) = P [ X 1 x 1 , . . . X n x n ] . Its properties are similar to that in the two-dimensional case, but it becomes increasingly difficult to write them down in mathematical notations. You can try to write down the 4th property of joint cdfs for fun! It is important to know how to get marginal distributions from the joint distribution. As before, we get F X 1 ( x 1 ) = lim x i →∞ ,i 6 =1 F X 1 , ··· ,X n ( x 1 , . . . , x n ) = F X 1 , ··· ,X n ( x 1 , , . . . , ) . More generally we can get the marginal of X k as F X k ( x k ) = lim x i →∞ ,i 6 = k F X 1 , ··· ,X n ( x 1 , . . . , x n ) = F X 1 , ··· ,X n ( , . . . , , x k , , . . . , ) . We can get the marginal distributions of the random variables from their joint cdf, but again we can not get full information on their joint behaviour or joint cdf, just by knowing their marginals. Similarly we can also try to get higher dimensional marginals from the joint cdf. The joint cdf of ( X 1 , X 2 ) can be obtained by F X 1 ,X 2 ( x 1 , x 2 ) = F X 1 , ··· ,X n ( x 1 , x 2 , , . . . , ) .

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• Summer '08
• WEBER
• Probability theory, probability density function, Cumulative distribution function, independent random variables

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Lecture_8 - 1 HIGHER DIMENSIONAL RANDOM VECTORS Lecture 8...

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