BETACDF Function
Evaluates the beta probability distribution function.
Usage
result = BETACDF(x, pin, qin)
Input Parameters
x—Argument for which the beta probability distribution function is to be evaluated. If Inverse is specified, argument x represents the probability for which the inverse of the Beta distribution function is to be evaluated. In this case, x must be in the open interval (0.0, 1.0).
pin—First beta distribution parameter. Parameter pin must be positive.
qin—Second beta distribution parameter. Parameter qin must be positive.
Returned Value
result—The probability that a beta random variable takes on a value less than or equal to x.
Input Keywords
Double—If present and nonzero, double precision is used.
Inverse—If present and nonzero, evaluates the inverse of the Beta distribution function. If Inverse is specified, argument x represents the probability for which the inverse of the Beta distribution function is to be evaluated. In this case, x must be in the open interval (0.0, 1.0).
Discussion
Function BETACDF evaluates the distribution function of a beta random variable with parameters pin and qin. This function is sometimes called the incomplete beta ratio and is denoted by Ix(p, q), where p = pin and q = qin. It is given by:
where Γ(·) is the gamma function. The value of the distribution function by Ix(p, q) is the probability that the random variable takes a value less than or equal to x.
The integral in the expression above is called the incomplete beta function and is denoted by βx(p, q). The constant in the expression is the reciprocal of the beta function (the incomplete function evaluated at 1) and is denoted by βx(p, q).
If the keyword Inverse is specified, the BETACDF function evaluates the inverse distribution function of a beta random variable with parameters pin and qin. With P = x, p = pin and q = qin, it returns x such that:
where Γ(·) is the gamma function. The probability that the random variable takes a value less than or equal to x is P.
The BETCDF function uses the method of Bosten and Battiste (1974).
Example
Suppose X is a beta random variable with parameters 12 and 12 (X has a symmetric distribution). This finds the probability that X is less than 0.6 and the probability that X is between 0.5 and 0.6. (Since X is a symmetric beta random variable, the probability that it is less than 0.5 is 0.5.)
; Call BETACDF to compute first probability and output results.
p = BETACDF(.6, 12, 12)
PM, p, Title = 'The probability that X is less than ' + $
'0.6 is:', Format= '(f8.4)'
; PV-WAVE prints the following:
; The probability that X is less than 0.6 is: 0.8364
; Call BETACDF and use the previously computed
; probability to determine the next probability.
p = p - BETACDF(.5, 12, 12)
PM, p, Format = '(f8.4)', title = 'The probability that X ' + $
'is between 0.5 and 0.6 is:'
; PV-WAVE prints the following:
; The probability that X is between 0.5 and 0.6 is: 0.3364
