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CS 475/575 Slide Set 7

CS 475/575 Slide Set 7. Random Variates 2 M. Overstreet Spring 2005. Uniform distribution. pdf cdf When x=a, F(x)=0, and C=(-a)/(b-a), so F(x) = (x-a)/(b-a) so r=(x-a)/(b-a) or x=r(b-a)+a where r is U(0,1). Exponential distribution - 1. Assumptions:

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CS 475/575 Slide Set 7

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  1. CS 475/575 Slide Set 7 Random Variates 2 M. Overstreet Spring 2005

  2. Uniform distribution pdf cdf When x=a, F(x)=0, and C=(-a)/(b-a), so F(x) = (x-a)/(b-a) so r=(x-a)/(b-a) or x=r(b-a)+a where r is U(0,1)

  3. Exponential distribution - 1 Assumptions: 1. The probability that an event occurs during is 2. is constant and independent of t. 3. The probability that more than one event occurs in approaches 0 as

  4. Exponential dist. - 2 If X is the length of time between successive events then X is said to have an exponential distribution and its PDF is The CDF is

  5. more exp.

  6. even more exp. Note that since F is expressible in closed form (that is, ), we can find : Since (1-r) is identically distributed to r, we use

  7. Normal Recall: Cannot use inverse CDF technique since we have not closed form representation, but do have at least 2 techniques.

  8. Normal - technique 1 Recall central limit theorem: the probability distribution of the sum of N iid random variates with mean  and variance tends toward a normal distribution with mean N and variance /n.

  9. normal - tech 1 cont. Thus if Since for U(a,b),  =(a+b)/2, so pick k=12 to get Problem: not good beyond

  10. Normal - technique 2 Requires generation of normals in pairs: 1. Generate two U(0,1) random variates, 2. Let 3. Let

  11. using Arena to fit distributions • see text, pp. 156-163 • steps: • open Arena • from Tools, select Input Analyzer • select “File,” “new”, then “use existing” icon. • select fit, then “fit all” or a particular distribution

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