The spherical distributions generate random vectors, located on a , and their inverses for the unit Gaussian double gsl_cdf_ugaussian_P ( double x ) ¶ĭouble gsl_cdf_ugaussian_Q ( double x ) ¶ĭouble gsl_cdf_ugaussian_Pinv ( double P ) ¶ĭouble gsl_cdf_ugaussian_Qinv ( double Q ) ¶
These functions compute the cumulative distribution functionsĭistribution with standard deviation sigma. double gsl_cdf_gaussian_P ( double x, double sigma ) ¶ĭouble gsl_cdf_gaussian_Q ( double x, double sigma ) ¶ĭouble gsl_cdf_gaussian_Pinv ( double P, double sigma ) ¶ĭouble gsl_cdf_gaussian_Qinv ( double Q, double sigma ) ¶ TheyĪre equivalent to the functions above with a standard deviation of one, These functions compute results for the unit Gaussian distribution. double gsl_ran_ugaussian ( const gsl_rng * r ) ¶ĭouble gsl_ran_ugaussian_pdf ( double x ) ¶ĭouble gsl_ran_ugaussian_ratio_method ( const gsl_rng * r ) ¶ Ziggurat algorithm is the fastest available algorithm in most cases. Marsaglia-Tsang ziggurat and Kinderman-Monahan-Leva ratio methods. This function computes a Gaussian random variate using the alternative double gsl_ran_gaussian_ziggurat ( const gsl_rng * r, double sigma ) ¶ĭouble gsl_ran_gaussian_ratio_method ( const gsl_rng * r, double sigma ) ¶ This function computes the probability density at xįor a Gaussian distribution with standard deviation sigma, using double gsl_ran_gaussian_pdf ( double x, double sigma ) ¶
Gaussian software result unit generator#
This function uses the Box-Muller algorithm which requires twoĬalls to the random number generator r. Gsl_ran_gaussian() to obtain a Gaussian distribution with mean Transformation on the numbers returned by Return a value of type unsigned int, and on most platforms this Note that the discrete random variate functions always TheĬorresponding cumulative distribution functions are declared in The functions for random variates and probability density functionsĭescribed in this section are declared in gsl_randist.h.
Gaussian software result unit full#
The cumulative distribution functions and their inverses areĬomputed separately for the upper and lower tails of the distribution,Īllowing full accuracy to be retained for small results. The library also provides cumulative distribution functions and inverseĬumulative distribution functions, sometimes referred to as quantileįunctions. This usually requires several samples from the generator. More complicated distributions are createdīy the acceptance-rejection method, which compares the desiredĭistribution against a distribution which is similar and knownĪnalytically. In the simplest cases a non-uniform distribution can be obtainedĪnalytically from the uniform distribution of a random number generatorīy applying an appropriate transformation. Random number generators in the library as an underlying source of Samples from theĭistributions described in this chapter can be obtained using any of the This chapter describes functions for generating random variates andĬomputing their probability distributions.
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