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A Summary of Random Variable Simulation

Uniform. X is uniformly distributed on the interval [a,b]. We write X~unif(a,b). Uses. the basis for generating all random variables. can be used as a model for a quantity that is known to vary between a and b for which little else is known. Uniform. method of generation. use a random number generat

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A Summary of Random Variable Simulation

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    1. A Summary of Random Variable Simulation Ideas for Today and Tomorrow

    2. Uniform X is uniformly distributed on the interval [a,b]

    3. Uniform method of generation

    4. Normal X is normally distributed with mean and variance

    5. Normal method of generation

    6. The Polar-Marsaglia Method

    7. Exponential

    8. Exponential method of generation

    9. Double Exponential X has a bilateral (double) exponential distribution with location parameter and shape parameter

    10. method of generation Double Exponential

    11. Gamma X has the gamma distribution with shape parameter and scale parameter

    12. Gamma method of generation

    13. Weibull X has the Weibull distribution with shape parameter and scale parameter

    14. Weibull method of generation

    15. Beta X has the beta distribution with parameters and

    16. Beta method of generation

    17. Pareto X has the Pareto distribution with parameter

    18. Pareto method of generation

    19. Cauchy X has the Cauchy distribution with location parameter and scale parameter

    20. Cauchy method of generation

    21. logistic X has the logistic distribution with location parameter and scale parameter

    22. logistic method of generation

    23. Gumbel X has the Gumbel distribution with location parameter and scale parameter

    24. Gumbel method of generation

    25. Log-Normal We write X~LN( , )

    26. Log-Normal method of generation

    27. Poisson counts the number of events that occur in a unit of time when events are occurring at a constant rate

    28. Poisson Specifically, to generate a Poisson rv with rate ,we will generate exponential rate inter- arrival times.

    29. Poisson Algorithm:

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