brooks

  1. Nicole Seaman

    P1.T2.602. Bootstrapping (Brooks)

    Learning objectives: Describe the bootstrapping method and its advantage over Monte Carlo simulation. Describe the pseudo-random number generation method and how a good simulation design alleviates the effects the choice of the seed has on the properties of the generated series. Describe...
  2. Nicole Seaman

    P1.T2.601. Variance reduction techniques (Brooks)

    Learning objectives: Explain how to use antithetic variate technique to reduce Monte Carlo sampling error. Explain how to use control variates to reduce Monte Carlo sampling error and when it is effective. Describe the benefits of reusing sets of random number draws across Monte Carlo...
  3. Nicole Seaman

    P1.T2.600. Monte Carlo simulation, sampling error (Brooks)

    Learning objectives: Describe the basic steps to conduct a Monte Carlo simulation. Describe ways to reduce Monte Carlo sampling error. Questions: 600.1. Although simulation methods might be employed in each of the following situations (or "use cases"), which situation below LEAST requires the...
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