MixtureDistribution[{w_1, ..., w_n}, {dist_1, ..., dist_n}] represents a mixture distribution whose CDF is given as a sum of the CDFs of the component distributions dist_i, ...

Derived distributions are modifications to existing distributions. There is a variety of ways in which you can arrive at modified distributions, including functions of random ...

MaxMixtureKernels is an option for SmoothKernelDistribution and related functions that specifies the maximum number and location of kernel functions to use in the estimation.

The fundamental type of distribution in reliability analysis is a lifetime distribution. This models the lifetime of a component or a system. Many lifetime distributions are ...

ParameterMixtureDistribution[dist[\[Theta]], \[Theta] \[Distributed] wdist] represents a parameter mixture distribution where the parameter \[Theta] is distributed according ...

MarginalDistribution[dist, k] represents a univariate marginal distribution of the k\[Null]^th coordinate from the multivariate distribution dist.MarginalDistribution[dist, ...

EstimatedDistribution[data, dist] estimates the parametric distribution dist from data.EstimatedDistribution[data, dist, {{p, p_0}, {q, q_0}, ...}] estimates the parameters ...

Likelihood[dist, {x_1, x_2, ...}] gives the likelihood function for observations x_1, x_2, ... from the distribution dist.

FindThreshold[image] finds a global threshold value that partitions the intensity values in image into two intervals.

LogLikelihood[dist, {x_1, x_2, ...}] gives the log-likelihood function for observations x_1, x_2, ... from the distribution dist.