Discrete distributions come from a variety of backgrounds, but perhaps the most common relate back to the simple Bernoulli trial, which chooses between two outcomes, called ...

Statistical distributions have applications in many fields, including the biological, social, and physical sciences. Mathematica represents statistical distributions as ...

BernoulliGraphDistribution[n, p] represents a Bernoulli graph distribution for n-vertex graphs with edge probability p.

Mathematica's sophisticated algorithms for handling higher mathematical functions to arbitrary precision—and in symbolic form—immediately brings a new level of accuracy—and ...

Combinatorial functions. The factorial function n! gives the number of ways of ordering n objects. For non-integer n, the numerical value of n! is obtained from the gamma ...

LogitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial logistic regression model of the form 1/(1 + E -(\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...)) ...

ProbitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial probit regression model of the form 1/2 (1 + erf((\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...

As the basis for many other special functions, Mathematica supports efficient arbitrary-precision evaluation of gamma functions, as well as an extensive web of relations and ...

 

GeneralizedLinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a generalized linear model of the form g -1 (\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ...) that ...