# 29 - Finite Probability(cntd Introduction Discrete...

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Introduction Finite Probability (cntd) Discrete Mathematics Andrei Bulatov

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Discrete Mathematics - Integers 29-2 Previous Lecture Experiment, outcomes, sample space Events Classic probability Likelihood of outcomes
Discrete Mathematics – Integers 29-3 More General Probability Sample space: Any set S Event: `Any’ subset of S Probability: A measure, that is a function Pr: P(S) [0,1], such that - Pr( ) = 0 - Pr(S) = 1 - Pr(A) 0 for all A S - for any disjoint A,B S, Pr(A B) = Pr(A) + Pr(B)

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Discrete Mathematics – Integers 29-4 More General Probability: Crooked Dice Suppose we made a loaded dice S = {1,2,3,4,5,6} Pr(1) = 1/16, Pr(2) = Pr(3) = Pr(4) = Pr(5) = 1/8 Pr(6) = 7/16 Pr({i,j,…,m}) = Pr(i) + Pr(j) + … + Pr(m) Find Pr({1,3,5})
29-5 More General Probability: Geometric Probability How to measure the area of an island? Draw a rectangle around the island and drop many random points Then Sample space: Points in the rectangle Events: Measurable sets of points

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## This note was uploaded on 10/14/2011 for the course MACM 101 taught by Professor Pearce during the Spring '08 term at Simon Fraser.

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29 - Finite Probability(cntd Introduction Discrete...

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