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 + \ ...
Series
(Built-in Mathematica Symbol) Series[f, {x, x_0, n}] generates a power series expansion for f about the point x = x_0 to order (x - x_0) n. Series[f, {x, x_0, n_x}, {y, y_0, n_y}, ...] successively finds ...
TransferFunctionModel[m, var] represents the model of the transfer-function matrix m with complex variable var.TransferFunctionModel[{num, den}, var] specifies the numerator ...
Mathematica provides built-in support for both programmatic and interactive image processing, fully integrated with Mathematica's powerful mathematical and algorithmic ...
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 ...
HistogramDistribution[{x_1, x_2, ...}] represents the probability distribution corresponding to a histogram of the data values x_i.HistogramDistribution[{{x_1, y_1, ...}, ...
LinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a linear model of the form \[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ... that fits the y_i for successive x ...
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 ...
ParameterMixtureDistribution[dist[\[Theta]], \[Theta] \[Distributed] wdist] represents a parameter mixture distribution where the parameter \[Theta] is distributed according ...
ProductDistribution[dist_1, dist_2, ...] represents the joint distribution with independent component distributions dist_1, dist_2, ....