Blurb::
Global reliability methods
Description::
These methods do not support
forward/inverse mappings involving \c reliability_levels, since they
never form a reliability index based on distance in u-space. Rather
they use a Gaussian process model to form an approximation to the
limit state (based either in x-space via the \c x_gaussian_process
specification or in u-space via the \c u_gaussian_process
specification), followed by probability estimation based on multimodal
adaptive importance sampling (see 
\cite Bichon2007) and 
\cite Bichon2008). These
probability estimates may then be transformed into generalized
reliability levels if desired. At this time, inverse reliability
analysis (mapping probability or generalized reliability levels into
response levels) is not implemented. 

The Gaussian process model approximation to the limit state is formed
over the aleatory uncertain variables by default, but may be extended
to also capture the effect of design, epistemic uncertain, and state
variables. If this is desired, one must use the appropriate controls
to specify the active variables in the variables specification
block.  Refer to \ref topic-variable_support for additional information
on supported variable types.

Topics::	uncertainty_quantification, reliability_methods
Examples::
Theory::
Faq::
See_Also::	method-adaptive_sampling, method-gpais, method-local_reliability, method-sampling, method-importance_sampling, method-polynomial_chaos, method-stoch_collocation
