Simple Random Sampling

Simple Random Sampling is the basic, most commonly used Monte Carlo simulation technique.

The general steps for implementing a Monte Carlo simulation using Simple Random Sampling are as follows:

  1. Identify the random variables. Assume appropriate distributions, and define properties for each (mean, standard deviation, or coefficient of variation).
  2. Specify the number of simulations to be executed (often 1,000; however, 10,000 or more simulations are necessary for accurate prediction of response statistical properties).
  3. Generate uniformly distributed random numbers for each random variable.
  4. Convert each uniform random number to a random variable value corresponding to the appropriate distribution.
  5. Simulate the design/process (execute system analysis) using the current values for random variables and the design variables.
  6. Repeat Step 3 through Step 5 for the number of simulations specified in Step 2.
  7. Perform postprocessing by analyzing statistics of responses (mean, standard deviation, range, distribution shape), and evaluate/rank the random variable effects.