chap5f - Copyright © 2005 by K.S. Trivedi 1 Probability...

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Unformatted text preview: Copyright © 2005 by K.S. Trivedi 1 Probability and Statistics with Reliability, Queuing and Computer Science Applications Second edition by K.S. Trivedi Publisher-John Wiley & Sons Chapter 5: Conditional Distribution and Expectation Dept. of Electrical & Computer Engineering Duke University Email: kst@ee.duke.edu URL: www.ee.duke.edu/~kst Copyright © 2005 by K.S. Trivedi 2 ependence among Random Variables ¡ So far we assumed that random variables are mutually independent ¡ Dependence arises quite commonly in practice ¡ We start by studying two random variables that have dependence ¡ This will lead us to a family of random variables (a stochastic process) in the next chapter ¡ Our primary tool for dealing with dependence is conditioning and the theorem of total probability with many of its variants Copyright © 2005 by K.S. Trivedi 3 Four Cases ¡ We begin by considering four cases that arise with two random variables X and Y ¡ X and Y both discrete (case 1) ¡ X and Y both continuous (case 2) ¡ X discrete and Y continuous (case 3) ¡ Y discrete and X continuous (case 4) ¡ We will be conditioning on X Copyright © 2005 by K.S. Trivedi 4 Conditional pmf (Case 1) ¡ Conditional probability: ¡ Above works if X is a discrete rv. ¡ For discrete rv’s X and Y , conditional pmf is, ¡ Above relationship also implies, ¡ Hence we have another version of the Theorem of Total Probability (TTP) ) ) ( ( ≠ = x X P Copyright © 2005 by K.S. Trivedi 5 Independence, Conditional Distribution ¡ ¡ Conditional distribution function ¡ Using conditional pmf we get the conditional distribution function, Copyright © 2005 by K.S. Trivedi 6 Case 1: X &Y both discrete rv ¡ Splitting a Poisson stream B A p 1-p Bernoulli trial p : prob. that the next job goes to server A k jobs n jobs Poisson job Stream with rate λ ¡ k out of n incoming jobs sent to server A: binomial pmf ¡ Hence using the theorem of total pmfs ` Copyright © 2005 by K.S. Trivedi 7 Another Example of Case 1 ¡ Software Reliability Growth Models ¡ Failure data is collected during testing ¡ Calibrate a reliability growth model using failure data; this model is then used for prediction ¡ Many SRGMs exist (JM,NHPP,HGRGM (chapters 2 & 3), Littlewood-Verrall, etc.) Copyright © 2005 by K.S. Trivedi 8 Randomness in Using Software ¡ P:I Æ O I: Input space O: Outputs P: Program ¡ Assume a subset of I is the error space E ¡ The sequence of applied inputs, starting from some initial input, takes a random amount of time to reach E Copyright © 2005 by K.S. Trivedi 9 A Program with a Single Fault ¡ Let F(t) be the distribution of time to reach the error space E starting from the initial applied input ¡ F(t) is then the distribution of time to find this bug during testing ¡ After t time units of testing, p=F(t) is the probability of finding this bug and 1-p = 1- F(t) of not finding that bug ¡ Mean number of faults found by time t (mean value function): m(t)=p=F(t) Copyright © 2005 by K.S. TrivediCopyright © 2005 by K....
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chap5f - Copyright © 2005 by K.S. Trivedi 1 Probability...

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