Triangular Distribution

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Triangular Distribution

The Triangular Distribution is typically used as a subjective description of a population for whichthere is only limited sample data. It is based on a knowledge of the minimum and maximum andan inspired guess as to what the modal value might be. Despite being a simplistic description of apopulation, it is a very useful distribution for modeling processes where the relationship betweenvariables is known, but data is scarce (possibly because of the high cost of collection).

It is also used as an alternative to the Beta distribution in PERT, CPM and similar forms of projectmanagement tool. The section on the Beta distribution contains an example using both the Betaand Triangular distributions.

Profile

Parameters

Parameter Description Characteristics

min Minimum value A float > -8 and <=mode

mode Modal Value A float >= min or <=max

max Maximum value A float >= mode and <8

Range

The range is determined by the min and max parameters.

Functions

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Triangular Distribution

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Properties

Example

Triangular distributions are used in oil and gas exploration where data is expensive to collect andit is almost impossible to model the population being sampled accurately, thus subjectivity plays agreater role than in data rich sectors. The example below shows a subjective assessment of theexpected size of discoveries in the United Kingdom's East Midland's Basin:

See the section on the lognormal distribution for an alternative model.

Parameter Estimation

The parameters of a triangular distribution can be derived directly from the dataset which it isintended to describe or model. Provided the dataset does not contain any anomalous points, theminimum and maximum can be obtained by sorting the values in ascending order and selecting

Triangular Distribution

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the first and last points. Un less the mode is being set subjectively, there are a number of ways ofdetermining the mode including:

Use an algorithm such successive bisection

Use the Max, Min and Mean to estimate the mode

This example illustrates both approaches. The figures below are the porosity of rock samplesfrom deep bore holes. These are very expensive to collect and only a few are available to theanalyst who may be asked to provide input for some form of Monte-Carlo evaluation:

10.0%, 13.5%, 15.5%, 20.0%

The successive bisection algorithm estimates the mode as 14.5%. The mean value of theobservations is 14.75%, using this value together with the min and max into this formula sets themode as 14.25%:

A subjective estimate of the mode by a cautions analyst might set the mode at 12%.

Random Number Generation

Random number generation (referred to as R) for a triangular distribution can be performed bytransforming a continuous uniform variable in the range 0 to 1 (referred to as U) with thedistribution's inverse probability function:

r=g(u)

Using Basic style code, the function would be similar to:

u=rnd()

if u <= (mode-min)/(max-min) then r=min+sqrt(u*(max-min)*(mode-min))else r=max-sqrt((1-u)*(max-min)*(max-mode))end if

Storing the value from the random number function in the variable u is important because mostrandom number function return a new value each time they are called. Without the use of the uvariable, the statement would use one value for branching and another for calculation.

Page updated: 20-Jun-2008

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