MinStableDistribution[\[Mu], \[Sigma], \[Xi]] represents a generalized minimum extreme value distribution with location parameter \[Mu], scale parameter \[Sigma], and shape ...
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 + \ ...
The numerical method of lines is a technique for solving partial differential equations by discretizing in all but one dimension, and then integrating the semi-discrete ...
EstimatedDistribution[data, dist] estimates the parametric distribution dist from data.EstimatedDistribution[data, dist, {{p, p_0}, {q, q_0}, ...}] estimates the parameters ...
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 ...
NonlinearModelFit[{y_1, y_2, ...}, form, {\[Beta]_1, ...}, x] constructs a nonlinear model with structure form that fits the y_i for successive x values 1, 2, ... using the ...
NormalDistribution[\[Mu], \[Sigma]] represents a normal (Gaussian) distribution with mean \[Mu] and standard deviation \[Sigma].NormalDistribution[] represents a normal ...
ParameterMixtureDistribution[dist[\[Theta]], \[Theta] \[Distributed] wdist] represents a parameter mixture distribution where the parameter \[Theta] is distributed according ...
StableDistribution[type, \[Alpha], \[Beta], \[Mu], \[Sigma]] represents the stable distribution S_type with index of stability \[Alpha], skewness parameter \[Beta], location ...
TruncatedDistribution[{x_min, x_max}, dist] represents the distribution obtained by truncating the values of dist to lie between x_min and ...