lecture19 - CS 70 Discrete Mathematics for CS Spring 2007...

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Unformatted text preview: CS 70 Discrete Mathematics for CS Spring 2007 Luca Trevisan Lecture 19 Random Variables and Expectation Question : The homeworks of 20 students are collected in, randomly shufFed and returned to the students. How many students receive their own homework? To answer this question, we ¡rst need to specify the probability space: plainly, it should consist of all 20! permutations of the homeworks, each with probability 1 20! . [Note that this is the same as the probability space for card shufFing, except that the number of items being shufFed is now 20 rather than 52.] It helps to have a picture of a permutation. Think of 20 books lined up on a shelf, labeled from left to right with 1 , 2 , . . . , 20. A permutation π is just a reordering of the books, which we can describe just by listing their labels from left to right. Let’s denote by π i the label of the book that is in position i . We are interested in the number of books that are still in their original position, i.e., in the number of i ’s such that π i = i . These are often known as ¡xed points of the permutation. Of course, our question does not have a simple numerical answer (such as 6), because the number depends on the particular permutation we choose (i.e., on the sample point). Let’s call the number of ¡xed points X . To make life simpler, let’s also shrink the class size down to 3 for a while. The following table gives a complete listing of the sample space (of size 3! = 6), together with the corresponding value of X for each sample point. [We use our bookshelf convention for writing a permutation: thus, for example, the permutation 312 means that book 3 is on the left, book 1 in the center, and book 2 on the right. You should check you agree with this table.] permutation π value of X 123 3 132 1 213 1 231 312 321 1 Thus we see that X takes on values 0, 1 or 3, depending on the sample point. A quantity like this, which takes on some numerical value at each sample point, is called a random variable (or r.v. ) on the sample space. Defnition 19.1 (random variable) : A random variable X on a sample space Ω is a function that assigns to each sample point ω ∈ Ω a real number X ( ω ) . Until further notice, we’ll restrict out attention to discrete random variables: i.e., their values will be integers or rationals, rather than arbitrary real numbers. The r.v. X in our permutation example above is completely speci¡ed by its values at all sample points, as CS 70, Spring 2007, Lecture 19 1 given in the above table. (Thus, for example, X ( 123 ) = 3 etc.) Rather than the value at each sample point, we are usually more interested in the set of points at which the r.v. takes on some given value. Let a be any real number. Then the set { ω ∈ Ω : X ( ω ) = a } is an event in the sample space (why?). We usually abbreviate this event to simply “ X = a ”. Since X = a is an event, we can talk about its probability, Pr [ X = a ] . The collection of these probabilities, for all possible....
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This note was uploaded on 08/27/2008 for the course CS 1050 taught by Professor Huang during the Spring '05 term at Georgia Tech.

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lecture19 - CS 70 Discrete Mathematics for CS Spring 2007...

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