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Triangular distribution

 
Statistics Dictionary: triangular distribution

If X and Y are independent random variables each having the same uniform distribution then (X+Y) has a triangular distribution. In the case of a continuous random variable the graph of the probability density function is an isosceles triangle. In the case of a discrete random variable the graph of the probability function has a triangular shape. The diagram (see diagram overleaf) shows this for the case of throwing two ordinary dice. The most probable score is 7 (which results in a 'Chance' card when starting from 'Go' on a Monopoly board).




Triangular distribution. This is a graph of the distribution of the sum of the scores on two fair six-slide dice. The mode (corresponding to a probability of 6/36) occurs when the sum is 7.



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Wikipedia: Triangular distribution
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Triangular
Probability density function
Plot of the Triangular PMF
Cumulative distribution function
Plot of the Triangular CMF
parameters: a:~a\in (-\infty,\infty)
b:~b>a\,
c:~a\le c\le b\,
support: a \le x \le b \!
pdf: 
                \left\{
                  \begin{matrix}
                    \frac{2(x-a)}{(b-a)(c-a)} & \mathrm{for\ } a \le x \le c \\ & \\
                    \frac{2(b-x)}{(b-a)(b-c)} & \mathrm{for\ } c \le x \le b 
                  \end{matrix}
                \right.
cdf: 
                \left\{
                  \begin{matrix}
                    \frac{(x-a)^2}{(b-a)(c-a)} & \mathrm{for\ } a \le x \le c \\ & \\
                    1-\frac{(b-x)^2}{(b-a)(b-c)} & \mathrm{for\ } c \le x \le b 
                  \end{matrix}
                \right.
mean: \frac{a+b+c}{3}
median: 
                \left\{
                  \begin{matrix}
                    a+\frac{\sqrt{(b-a)(c-a)}}{\sqrt{2}} & \mathrm{for\ } c\!\ge\!\frac{b\!-\!a}{2}\\ & \\
                    b-\frac{\sqrt{(b-a)(b-c)}}{\sqrt{2}} & \mathrm{for\ } c\!\le\!\frac{b\!-\!a}{2} 
                  \end{matrix}
                \right.
mode: c\,
variance: \frac{a^2+b^2+c^2-ab-ac-bc}{18}
skewness: 
              \frac{\sqrt 2 (a\!+\!b\!-\!2c)(2a\!-\!b\!-\!c)(a\!-\!2b\!+\!c)}{5(a^2\!+\!b^2\!+\!c^2\!-\!ab\!-\!ac\!-\!bc)^\frac{3}{2}}
kurtosis: -\frac{3}{5}
entropy: \frac{1}{2}+\ln\left(\frac{b-a}{2}\right)
mgf: 2\frac{(b\!-\!c)e^{at}\!-\!(b\!-\!a)e^{ct}\!+\!(c\!-\!a)e^{bt}}
{(b-a)(c-a)(b-c)t^2}
cf: -2\frac{(b\!-\!c)e^{iat}\!-\!(b\!-\!a)e^{ict}\!+\!(c\!-\!a)e^{ibt}}
{(b-a)(c-a)(b-c)t^2}


In probability theory and statistics, the triangular distribution is a continuous probability distribution with lower limit a, mode c and upper limit b.

f(x|a,b,c)=\left\{
                      \begin{matrix}
                          \frac{2(x-a)}{(b-a)(c-a)} & \mathrm{for\ } a \le x \le c \\ & \\
                          \frac{2(b-x)}{(b-a)(b-c)} & \mathrm{for\ } c \le x \le b \\ & \\
                          0                         & \mathrm{otherwise}
                      \end{matrix}
                  \right.

Contents

Special cases

Two points known

The distribution simplifies when c = a or c = b. For example, if a = 0, b = 1 and c = 1, then the equations above become:

 \left.\begin{matrix}f(x) &=& 2x \\[8pt]
F(x) &=& x^2 \end{matrix}\right\} \text{ for } 0 \le x \le 1
 \begin{align}
  E(X) & = \frac{2}{3} \\[8pt]
  \mathrm{Var}(X) &= \frac{1}{18}
\end{align}

Distribution of two standard uniform variables

This distribution for a = 0, b = 1 and c = 0.5 is distribution of X = (X1 + X2)/2, where X1, X2 are two independent random variables with standard uniform distribution.


  f(x) = \begin{cases}
  4x   & \text{for }0 \le x < \frac{1}{2}   \\
  4-4x & \text{for }\frac{1}{2} \le x \le 1
  \end{cases}

  F(x) = \begin{cases}
  2x^2       & \text{for }0 \le x < \frac{1}{2} \\
  1-2(1-x)^2 & \text{for }\frac{1}{2} \le x \le 1
  \end{cases}

\begin{align}
E(X) & = \frac{1}{2} \\[6pt]
\operatorname{Var}(X) & = \frac{1}{24}
\end{align}

Distribution of the absolute difference of two standard uniform variables

This distribution for a = 0, b = 1 and c = 0 is distribution of X = |X1 − X2|, where X1, X2 are two independent random variables with standard uniform distribution.


\begin{align}
f(x) & = 2 - 2x \text{ for } 0 \le x < 1 \\[6pt]
F(x) & = 2x - x^2 \text{ for } 0 \le x < 1 \\[6pt]
E(X) & = \frac{1}{3} \\[6pt]
\operatorname{Var}(X) & = \frac{1}{18}
\end{align}

Generating Triangular-distributed random variates

Given a random variate U drawn from the uniform distribution in the interval (0, 1), then the variate


\begin{matrix}
\begin{cases}
X = a + \sqrt{U(b-a)(c-a)} & \text{ for } 0 < U < F(c) \\ & \\
X = b - \sqrt{(1-U)(b-a)(b-c)} & \text{ for } F(c) \le U < 1
\end{cases}
\end{matrix}

has a Triangular distribution with parameters a, b and c. This can be obtained from the cumulative distribution function.

Use of the distribution

The triangular distribution is typically used as a subjective description of a population for which there is only limited sample data, and especially in cases where the relationship between variables is known but data is scarce (possibly because of the high cost of collection). It is based on a knowledge of the minimum and maximum and an "inspired guess" [1] as to the modal value.

Business simulations

The triangular distribution is therefore often used in business decision making, particularly in simulations. Generally, when not much is known about the distribution of an outcome, (say, only its smallest and largest values) it is possible to use the uniform distribution. But if the most likely outcome is also known, then the outcome can be simulated by a triangular distribution. See for example under corporate finance.

Project management

The triangular distribution, along with the Beta distribution, is also widely used in project management (as an input into PERT and hence critical path method (CPM)) to model events which take place within an interval defined by a minimum and maximum value.

Audio dithering

The symmetric triangular distribution is commonly used in audio dithering, where it is called TPDF (Triangular Probability Density Function).

See also

External links


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Some good "Triangular distribution" pages on the web:


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Copyrights:

Statistics Dictionary. A Dictionary of Statistics. Second edition revised. Copyright © Oxford University Press, 2008. All rights reserved.  Read more
Wikipedia. This article is licensed under the Creative Commons Attribution/Share-Alike License. It uses material from the Wikipedia article "Triangular distribution" Read more