Relative distribution



Format: Relative(min, max, {x}, {p})

 

The Relative distribution is a non-parametric distribution (i.e. there is no underlying probability model) where {x} is an array of x-values with probability densities {p} and where the distribution falls between the minimum and maximum. An example of the Relative distribution is given below:

Uses

1. modeling expert opinion

The Relative distribution is very useful for producing a fairly detailed distribution that reflects an expert's opinion. The Relative distribution is the most flexible of all of the continuous distribution functions. It enables the analyst and expert to tailor the shape of the distribution to reflect, as closely as possible, the opinion of the expert.

2. modeling posterior distribution in Bayesian inference

If you use the construction method of obtaining a Bayesian posterior distribution, you will have two arrays: a set of possible value in ascending orde" and a set of posterior weights for each of those values. This exactly matches the input parameters for a Relative distribution which can then be used to generate values from the posterior distribution. Examples: Turbine blades; Fitting a Weibull distribution.

ModelRisk functions added to Microsoft Excel for the Relative distribution

VoseRelative generates random values from this distribution for Monte Carlo simulation, or calculates a percentile if used with a U parameter.

VoseRelativeObject constructs a distribution object for this distribution.

VoseRelativeProb returns the probability density or cumulative distribution function for this distribution.

VoseRelativeProb10 returns the log10 of the probability density or cumulative distribution function. 

 

Relative distribution equations

 

 

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