SurvivalFunction[dist, x] gives the survival function for the symbolic distribution dist evaluated at x.SurvivalFunction[dist, {x_1, x_2, ...}] gives the multivariate ...

InverseSurvivalFunction[dist, q] gives the inverse of the survival function for the symbolic distribution dist as a function of the variable q.

MarcumQ[m, a, b] gives Marcum's Q function Q_m (a, b).MarcumQ[m, a, b_0, b_1] gives Marcum's Q function Q_m (a, b_0) - Q_m (a, b_1).

SurvivalDistribution[{e_1, e_2, ...}] represents a survival distribution with event times e_i.SurvivalDistribution[{w_1, w_2, ...} -> {e_1, e_2, ...}] represents a survival ...

There are a variety of ways to describe probability distributions such as probability density or mass, cumulative versions of density and mass, inverses of the cumulative ...

HazardFunction[dist, x] gives the hazard function for the symbolic distribution dist evaluated at x.HazardFunction[dist, {x_1, x_2, ...}] gives the multivariate hazard ...

Probability and statistics are used to model uncertainty from a variety of sources, such as incomplete or simplified models. Yet you can build useful models for aggregate or ...

InverseCDF[dist, q] gives the inverse of the cumulative distribution function for the symbolic distribution dist as a function of the variable q.

CDF[dist, x] gives the cumulative distribution function for the symbolic distribution dist evaluated at x.CDF[dist, {x_1, x_2, ...}] gives the multivariate cumulative ...

MultinomialDistribution[n, {p_1, p_2, ..., p_m}] represents a multinomial distribution with n trials and probabilities p_i.