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

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).

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

FailureDistribution[bexpr, {{x_1, dist_1}, {x_2, dist_2}, ...}] represents the failure distribution for a system with events x_i having reliability distribution dist_i where ...

ReliabilityDistribution[bexpr, {{x_1, dist_1}, {x_2, dist_2}, ...}] represents the reliability distribution for a system with components x_i having reliability distribution ...

SurvivalDistribution[{e_1, e_2, ...}] represents a survival distribution with event times e_i. SurvivalDistribution[{cw_1, cw_2, ...} -> {e_1, e_2, ...}] represents a ...

StandbyDistribution[dist_1, {dist_2, ..., dist_n}] represents a standby distribution with component lifetime distributions dist_i. When component i fails, component i + 1 ...

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 ...

BooleanConsecutiveFunction[k, n] represents a Boolean function of n variables that gives True if k consecutive variables are True. BooleanConsecutiveFunction[{k, True}, n] ...

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 ...