exampleclass2

# Exampleclass2 - THE UNIVERSITY OF HONG KONG DEPARTMENT OF STATISTICS AND ACTUARIAL SCIENCE STAT1301 PROBABILITY AND STATISTICS I EXAMPLE CLASS 2

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Unformatted text preview: THE UNIVERSITY OF HONG KONG DEPARTMENT OF STATISTICS AND ACTUARIAL SCIENCE STAT1301 PROBABILITY AND STATISTICS I EXAMPLE CLASS 2 Review Definition of conditional probability For any two events A and B , the conditional probability of A given the occurrence of B is written as P ( A | B ) and is defined as P ( A | B ) = P ( A ∩ B ) P ( B ) provided that P ( B > 0). Multiplication theorem (a) For any two events A and B with P ( B ) > 0, P ( A ∩ B ) = P ( B ) P ( A | B ) . (b) For any three events A,B,C with P ( B ∩ C ) > 0, P ( A ∩ B ∩ C ) = P ( C ) P ( B | C ) P ( A | B ∩ C ) . Independence (a) Two events A and B are called independent if and only if P ( A ∩ B ) = P ( A ) P ( B ) . If P ( A ) > 0,then A and B are independent if P ( B | A ) = P ( B ). (b) The events A 1 ,A 2 , · · · ,A k are (mutually) independent if and only if the probability of the intersection of any combination of them is equal to the product of the probabilities of the corresponding single events. For example, A 1 ,A 2 ,A 3 are independent if and only if P ( A 1 ∩ A 2 ) = P ( A 1 ) P ( A 2 ) P ( A 1 ∩ A 3 ) = P ( A 1 ) P ( A 3 ) P ( A 2 ∩ A 3 ) = P ( A 2 ) P ( A 3 ) P ( A 1 ∩ A 2 ∩ A 3 ) = P ( A 1...
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## This note was uploaded on 01/16/2012 for the course STAT 1301 taught by Professor Smslee during the Fall '08 term at HKU.

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Exampleclass2 - THE UNIVERSITY OF HONG KONG DEPARTMENT OF STATISTICS AND ACTUARIAL SCIENCE STAT1301 PROBABILITY AND STATISTICS I EXAMPLE CLASS 2

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