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Unformatted text preview: Illinois Institute of Technology Department of Computer Science Lecture 10: February 25, 2008 CS 330 Discrete Structures Spring Semester, 2008 1 Probability When the weatherwoman says “there is a 30% chance of rain”, what does she mean? Does she mean that: • rain will fall on 30% of the viewing area? • in the last 100 years, it rained 30 times on this date? • under present conditions, recorded history for comparable conditions shows it rained 30% of the time? Similarly, what does it mean for an algorithm to be correct 99% of the time? Can we trust it to guard our nuclear warheads? In order to answer these questions, we first need to know a little about probability . 2 Basics of Probability Consider the following amoebalike graph: z }  { T o t a l i t y o f e v e r y t h i n g t h a t c o u l d p o s s i b l y h a p p e n z } { some possible event z }  { S u b s e t d e fi n e d b y a n e v e n t o r g r o u p o f e v e n t s S We wish to measure the likelihood of event S occurring. We call this “Probability of S ” and we write it as Pr { S } or P [ S ] . Consider the following experiment: We will flip a “fair” coin. We have the following “universe” of outcomes: CS 330—Spring, 2008 2 Lecture 10: February 25, 2008 H E A D S T A I L S Our intuition tells us that Pr { TAILS } is 50% or 1 2 because TAILS happens about half the time. Consider this experiment: We will roll a “fair” die. We have the following “universe” of outcomes: Our intuition tells us that Pr { Rolling a one } is 1 6 , because rolling a “one” occurs about one sixth of the time. From these we can see a simple definition of Probability as: Pr { S } = number of events in S number of events altogether This definition will be ample for our purposes....
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 Spring '08
 Reingold,EdwardM.
 Computer Science, Conditional Probability, Probability theory, urn, gray balls

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