Triangular distribution

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. 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 distribution simplifies when c = a or c = b. For example, if a = 0, b = 1 and c = 1, then the PDF and CDF become: This distribution for a = 0, b = 1 and c = 0 is the distribution of X = |X1 − X2|, where X1, X2 are two independent random variables with standard uniform distribution. The symmetric case arises when c = (a + b) / 2.In this case, an alternate form of the distribution function is: This distribution for a = 0, b = 1 and c = 0.5—the mode (i.e., the peak) is exactly in the middle of the interval—corresponds to the distribution of the mean of two standard uniform variables, i.e., the distribution of X = (X1 + X2) / 2, where X1, X2 are two independent random variables with standard uniform distribution in . Given a random variate U drawn from the uniform distribution in the interval (0, 1), then the variate where F ( c ) = ( c − a ) / ( b − a ) {displaystyle F(c)=(c-a)/(b-a)} , has a triangular distribution with parameters a , b {displaystyle a,b} and c {displaystyle c} . This can be obtained from the cumulative distribution function. 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' as to the modal value. For these reasons, the triangle distribution has been called a 'lack of knowledge' distribution. 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.