Random variables generated to conform to a specific probability distribution are fundamental to the monte-carlo process of algorithm testing and calibration. Tina provides a set of routines to support this approach. These are :

int rand_bit()Returns a random bit (i.e. 0 or 1 with probabilities 0.5).

int rand_int(int a, int b)Returns a uniformly distributed random integer x with a = x b, (i.e. x never takes the value b).

double rand_1()Returns a uniformly distributed random double between 0.0 and 1.0.

double rand_unif(double x, double y)Returns a uniformly distributed random double between x and y.

double rand_normal(double mu, double sigma)

Returns a normally distributed random double with mean mu and standard deviation sigma.

double chisq(double x, int n)Returns the confidence that a chi squared variable with n degrees of freedom is less than or equal to x.