Returns the probability density function of a distribution evaluated at a specified value. Via mean and standard deviation.

Syntax

=MM_PDF_MS("DistName", X, Mean, [StdDev], [Param3])

• "DistName"
  String. Name of the desired distribution. 

• X
  The value at which the probability density function is evaluated. 

• 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.

Remarks

• To check all supported distributions, see MM_DISTRIBUTIONSLIST function. 

• Use MM_PDF function to evaluate the probability density function via distribution parameters.
  Use MM_PDF_TRUNC to evaluate the probability density function of a truncated distribution (with distribution parameters).
  Use MM_PDF_MS_TRUNC to evaluate the probability density function of a truncated distribution (with mean and standard deviation). 

• See MM_CDF for cumulative distribution function.
  See MM_InvCDF for inverse cumulative distribution function. 

Example 1: Bernoulli distribution

Formula1: =MM_PDF_MS("Bernoulli",1,0.85)
Evaluates at 1 the probability density function of a Bernoulli distribution with a mean equal to 0.85. Returns 0.85. Note that this gives the same result as =MM_PDF("Bernoulli", 1,0.85) as the mean of the Bernoulli distribution is equal to the probability of success of the Bernoulli trial. 

Formula2: =MM_PDF_MS("Bernoulli", 1,0.85,2)
Evaluates at 1 the probability density function of a Bernoulli distribution with a parameter equal to 0.85. Returns 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_PDF_MS("Gamma",1.15,4,8)
Evaluates, at x equal to 1.15, the probability density function of a Gamma distribution with mean and standard deviation equal to 4 and 8, respectively. Returns approximately 0.12.