Hi all I'd like to compute the following integral: I = \int_xl^xu dx \int_yl^yu dy ... f(x, y, ...) g(x,y, ...) where f(x,y, ...) is any of the GSL probability density function and g(x,y, ...) is a function depending on random variates (x,y, ...).
As the GSL probability density functions allow us to either draw random variates according to the probability distribution of the density functions, or to get the value of the probability density functions at some points (x,y,...), I am wondering how to compute an approximation of the mean of g(x,y,...) according to the probability density function f(x,y,...). I am thinking about two ways of computing this integral: 1. Use the GSL Monte-Carlo integration methods to compute an approximation of the integral of the function h(x,y,...) = f(x,y,...) g(x,y,...), without using the fact that f(x,y,...) is a probability density function ; 2. Program my own Monte-Carlo integration algorithm (without the GSL Monte-Carlo methods) using f(x,y,...) as the Monte-Carlo integration's probability density function. The second way seems to me better than the first one, since the first way would compute an approximation of the mean of a random function using the Monte-Carlo method's probability density function, that has nothing to do with the random function's probability density function... On other hand, the second method requires to recode many things that are already provided by the GSL's Monte-Carlo integration methods. Am I right? What would you advise me? Thank you very much in advance for your help, Florent Teichteil. _______________________________________________ Help-gsl mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-gsl
