n19 - CS 70 Discrete Mathematics and Probability Theory...

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Unformatted text preview: CS 70 Discrete Mathematics and Probability Theory Fall 2010 Tse/Wagner Note 19 A Brief Introduction to Continuous Probability Up to now we have focused exclusively on discrete probability spaces , where the number of sample points is either finite or countably infinite (such as the integers). As a consequence we have only been able to talk about discrete random variables, which take on only a finite (or countably infinite) number of values. But in real life many quantities that we wish to model probabilistically are real-valued ; examples include the position of a particle in a box, the time at which a certain incident happens, or the direction of travel of a meteorite. In this lecture, we discuss how to extend the concepts weve seen in the discrete setting to this continuous setting. As we shall see, everything translates in a natural way once we have set up the right framework. The framework involves some elementary calculus. Continuous uniform probability spaces Suppose we spin a wheel of fortune and record the position of the pointer on the outer circumference of the wheel. Assuming that the circumference is of length and that the wheel is unbiased, the position is presumably equally likely to take on any value in the real interval [ , ] . How do we model this experiment using a probability space? Consider for a moment the (almost) analogous discrete setting, where the pointer can stop only at a finite number m of positions distributed evenly around the wheel. (If m is very large, then presumably this is in some sense similar to the continuous setting.) Then we would model this situation using the discrete sample space = { , m , 2 m ,..., ( m- 1 ) m } , with uniform probabilities Pr [ ] = 1 m for each . In the continuous world, however, we get into trouble if we try the same approach. If we let range over all real numbers in [ , ] , what value should we assign to each Pr [ ] ? By uniformity this probability should be the same for all , but then if we assign to it any positive value, the sum of all probabilities Pr [ ] for will be ! Thus Pr [ ] must be zero for all . But if all of our sample points have probability zero, then we are unable to assign meaningful probabilities to any events! To rescue this situation, consider instead any non-empty interval [ a , b ] [ , ] . Can we assign a non-zero probability value to this interval? Since the total probability assigned to [ , ] must be 1, and since we want our probability to be uniform, the logical value for the probability of interval [ a , b ] is length of [ a , b ] length of [ , ] = b- a ....
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This note was uploaded on 12/08/2010 for the course CS 70 taught by Professor Papadimitrou during the Fall '08 term at University of California, Berkeley.

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n19 - CS 70 Discrete Mathematics and Probability Theory...

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