Discrete Probability Models

# 3 probabilities prs1 prs2 prs3 assigned

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Unformatted text preview: s3 , . . .} 3 Probabilities: Pr(s1 ), Pr(s2 ), Pr(s3 ), . . ., assigned based on analysis of experiment, with each Pr(si ) ≥ 0 and Pr(s1 ) + Pr(s2 ) + Pr(s3 ) + . . . = 1 4 Probability of event: If E = {s1 , s2 , . . . , sk }, Pr(E ) = Pr(s1 ) + Pr(s2 ) + Pr(s3 ) + . . . + Pr(sk ) 5 Discrete uniform models: If Ω = {s1 , s2 , . . . , sn } (ﬁnite) and Pr(s1 ) = Pr(s2 ) = . . . = Pr(sn ) (= 1/n) then Pr(E ) = #(outcomes in E ) |E | = #(outcomes in Ω) n This was 17th century (Fermat, Pascal) deﬁnition of probability Math 30530 (Fall 2012) Discrete models August 28, 2013 2/2...
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## This note was uploaded on 01/31/2014 for the course MATH 30530 taught by Professor Hind,r during the Fall '08 term at Notre Dame.

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