Simulates a desired probability distribution using the mean and standard deviation. 

Syntax

=MM_SIMULATE_MS("DistName", Mean, [StdDev], [Param3])

• "DistName"
  String. Name of the distribution you want to simulate. 

• Mean
  Mean of the desired distribution. 

• StdDev (optional/required depending on the distribution)
  Standard deviation of the desired distribution. 

• Param3 (optional/required depending on the distribution)
  Third parameter of the distribution. To check the definition of the parameters, refer to the MM_PARAMNAME function.

Remarks

• To check all supported distributions, use the MM_DISTRIBUTIONSLIST function. 

• Use the MM_SIMULATE function to simulate a distribution using distribution parameters.

• Use the MM_SIMULATE_TRUNC to simulate a truncated distribution using distribution parameters.
  Use the MM_SIMULATE_MS_TRUNC to simulate a truncated distribution using the mean and standard deviation.

• A single cell with this formula will inherit from the "SIM" dimension, meaning that the cell will have as many simulated values as the number of simulations chosen before running the model. 

Example 1: Bernoulli distribution

Formula1: =MM_SIMULATE_MS("Bernoulli",0.85)
Simulates a Bernoulli distribution with mean 0.85; note that this gives the same result as =MM_SIMULATE("Bernoulli", 0.85) as the mean of this distribution is equal to the probability of success of the Bernoulli trial. 

Formula2: =MM_SIMULATE_MS("Bernoulli", 0.85,2)
Simulates a Bernoulli distribution with mean 0.85. Note that the standard deviation is equal to approximately 0.36 and not 2. As this probability distribution has only one degree of freedom, the StdDev parameter is ignored. 

Example 2: Gamma distribution

Formula3: =MM_SIMULATE_MS("Gamma",4,8)
Simulates a Gamma distribution with mean and standard deviation equal to 4 and 8, respectively.