Triangular distribution

Triangular distribution

Probability distribution
name =Triangular
type =density
pdf_

cdf_

parameters =a:~ain (-infty,infty)
b:~b>a,
c:~ale cle b,
support =a le x le b !
pdf = left{ egin{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} ight.
cdf = left{ egin{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} ight.
mean =frac{a+b+c}{3}
median = left{ egin{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} ight.
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}+lnleft(frac{b-a}{2} ight)
mgf =2frac{(b!-!c)e^{at}!-!(b!-!a)e^{ct}!+!(c!-!a)e^{bt{(b-a)(c-a)(b-c)t^2}
char =-2frac{(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{ egin{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{for any other case} end{matrix} ight.

pecial 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.egin{matrix}f(x) &=& 2x \ \ F(x) &=& x^2 end{matrix} ight} mathrm{for } 0 le x le 1

: egin{matrix} E(X) &=& frac{2}{3} \ & & \ mathrm{Var}(X) &=& frac{1}{18}end{matrix}

Distribution of two standard uniform variables

This distribution for "a"=0, "b"=1 and "c"=0.5 is distribution of X = frac{X_1+X_2}{2} , where X_1, X_2 are two random variables with standard uniform distribution.

: f(x)=left{egin{matrix} 4x & mathrm{for }0 le x < frac{1}{2} \ \ 4-4x & mathrm{for }frac{1}{2} le x le 1 end{matrix} ight.

: F(x)=left{egin{matrix} 2x^2 & mathrm{for }0 le x < frac{1}{2} \ \ 1-2(1-x)^2 & mathrm{for }frac{1}{2} le x le 1 end{matrix} ight.

: egin{matrix} E(X) &=& frac{1}{2} \ \ mathrm{Var}(X) &=& frac{1}{24}end{matrix}

Distribution of the absolute difference of two standard uniform variables

This distribution for "a"=0, "b"=1 and "c"=0 is distribution of X = | X_1-X_2 | , where X_1, X_2 are two random variables with standard uniform distribution.

: egin{matrix} f(x)&=& 2 - 2x qquad mathrm{for } 0 le x < 1 \ \

F(x) &=& 2x - x^2 qquad mathrm{for } 0 le x < 1 \ \ end{matrix}

: egin{matrix} E(X) &=& frac{1}{3} \ \ mathrm{Var}(X) &=& frac{1}{18}end{matrix}

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" [http://www.decisionsciences.org/DecisionLine/Vol31/31_3/31_3clas.pdf] 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).

ee also

*Thomas Simpson

External links

*MathWorld|urlname=TriangularDistribution|title=Triangular Distribution
* [http://www.decisionsciences.org/DecisionLine/Vol31/31_3/31_3clas.pdf Triangle Distribution] , decisionsciences.org
* [http://www.brighton-webs.co.uk/distributions/triangular.asp Triangular Distribution] , brighton-webs.co.uk


Wikimedia Foundation. 2010.

Игры ⚽ Поможем написать курсовую

Look at other dictionaries:

  • Uniform distribution (continuous) — Uniform Probability density function Using maximum convention Cumulative distribution function …   Wikipedia

  • Beta distribution — Probability distribution name =Beta| type =density pdf cdf parameters =alpha > 0 shape (real) eta > 0 shape (real) support =x in [0; 1] ! pdf =frac{x^{alpha 1}(1 x)^{eta 1 {mathrm{B}(alpha,eta)}! cdf =I x(alpha,eta)! mean… …   Wikipedia

  • Irwin-Hall distribution — In probability theory and statistics, the Irwin Hall distribution is a continuous probability distribution of the sum of n i.i.d. U (0,1) random variables::X = sum {k=1}^n U k.For this reason it is also known as the uniform sum distribution. The… …   Wikipedia

  • Multinomial distribution — Multinomial parameters: n > 0 number of trials (integer) event probabilities (Σpi = 1) support: pmf …   Wikipedia

  • Cauchy distribution — Not to be confused with Lorenz curve. Cauchy–Lorentz Probability density function The purple curve is the standard Cauchy distribution Cumulative distribution function …   Wikipedia

  • Normal distribution — This article is about the univariate normal distribution. For normally distributed vectors, see Multivariate normal distribution. Probability density function The red line is the standard normal distribution Cumulative distribution function …   Wikipedia

  • Probability distribution — This article is about probability distribution. For generalized functions in mathematical analysis, see Distribution (mathematics). For other uses, see Distribution (disambiguation). In probability theory, a probability mass, probability density …   Wikipedia

  • Negative binomial distribution — Probability mass function The orange line represents the mean, which is equal to 10 in each of these plots; the green line shows the standard deviation. notation: parameters: r > 0 number of failures until the experiment is stopped (integer,… …   Wikipedia

  • Exponential distribution — Not to be confused with the exponential families of probability distributions. Exponential Probability density function Cumulative distribution function para …   Wikipedia

  • Chi-squared distribution — This article is about the mathematics of the chi squared distribution. For its uses in statistics, see chi squared test. For the music group, see Chi2 (band). Probability density function Cumulative distribution function …   Wikipedia

Share the article and excerpts

Direct link
Do a right-click on the link above
and select “Copy Link”