Triangular distribution

From Wikipedia, the free encyclopedia
Jump to: navigation, search
Triangular
Probability density function
Plot of the Triangular PMF
Cumulative distribution function
Plot of the Triangular CMF
Parameters a:~a\in (-\infty,\infty)
b:~a<b\,
c:~a\le c\le b\,
Support a \le x \le b \!
pdf 
  \begin{cases}
    0 & \mathrm{for\ } x < a, \\
    \frac{2(x-a)}{(b-a)(c-a)} & \mathrm{for\ } a \le x \leq c, \\[4pt]
    \frac{2(b-x)}{(b-a)(b-c)} & \mathrm{for\ } c < x \le b, \\[4pt]
    0 & \mathrm{for\ } b < x.
  \end{cases}
CDF 
  \begin{cases}
    0 & \mathrm{for\ } x < a, \\[2pt]
    \frac{(x-a)^2}{(b-a)(c-a)} & \mathrm{for\ } a \le x \leq c, \\[4pt]
    1-\frac{(b-x)^2}{(b-a)(b-c)} & \mathrm{for\ } c < x \le b, \\[4pt]
    1 & \mathrm{for\ } b < x.
  \end{cases}
Mean \frac{a+b+c}{3}
Median 
  \begin{cases}
    a+\frac{\sqrt{(b-a)(c-a)}}{\sqrt{2}} & \mathrm{for\ } c \ge \frac{a+b}{2}, \\[6pt]
    b-\frac{\sqrt{(b-a)(b-c)}}{\sqrt{2}} & \mathrm{for\ } c \le \frac{a+b}{2}.
  \end{cases}
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}}
Ex. 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, upper limit b and mode c, where a < b and a ≤ c ≤ b. The probability density function is given by Probability density function for the triangular distribution.
whose cases avoid division by zero if c = a or c = b.

Special cases[edit]

Two points known[edit]

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 mean of two standard uniform variables[edit]

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[edit]

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[edit]

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}
[1]

Where F(c) = (c-a)/(b-a)

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

Use of the distribution[edit]

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" [2] as to the modal value. For these reasons, the triangle distribution has been called a "lack of knowledge" distribution.

Business simulations[edit]

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[edit]

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[edit]

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

See also[edit]

References[edit]

External links[edit]