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, ...

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

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 ...

When building an initial statistical model, you may not have a good idea of what parametric distribution family it should come from. Nonparametric distributions make very few ...

SplicedDistribution[{w_1, w_2, ..., w_n}, {c_0, c_1, ..., c_n}, {dist 1, dist_2, ..., dist_n}] represents the distribution obtained by splicing the distributions dist_1, ...

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 ...

Reliability of a component is the probability that it will function for a specified period of time. This is modeled as a lifetime distribution. A system built from ...

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 ...