global_evidence
Evidence theory with evidence measures computed with global optimization methods
Topics
epistemic_uncertainty_quantification_methods, evidence_theory
Specification
Alias: nond_global_evidence
Arguments: None
Child Keywords:
Required/Optional 
Description of Group 
Dakota Keyword 
Dakota Keyword Description 

Optional 
Number of samples for samplingbased methods 

Optional 
Seed of the random number generator 

Optional (Choose One) 
Solution Approach 
Use the surrogate based optimization method 

Use the Efficient Global Optimization method 

Use an evolutionary algorithm 

Uses Latin Hypercube Sampling (LHS) to sample variables 

Optional 
Values at which to estimate desired statistics for each response 

Optional 
Specify probability levels at which to estimate the corresponding response value 

Optional 
Specify generalized relability levels at which to estimate the corresponding response value 

Optional 
Selection of cumulative or complementary cumulative functions 

Optional 
Selection of a random number generator 

Optional 
Identifier for model block to be used by a method 
Description
global_evidence
allows the user to specify
several global approaches for calculating the belief and plausibility functions:
lhs
 note: this takes the minimum and maximum of the samples as the bounds per “interval cell combination.”ego
 uses Efficient Global Optimization which is based on an adaptive Gaussian process surrogate.sbo
 uses a Gaussian process surrogate (nonadaptive) within an optimization process.ea
 uses an evolutionary algorithm. This can be expensive as the ea will be run for each interval cell combination.
Note that to calculate the plausibility and belief cumulative
distribution functions, one has to look at all combinations of
intervals for the uncertain variables. In terms of implementation, if
one is using LHS sampling as outlined above, this method creates a
large sample over the response surface, then examines each cell to
determine the minimum and maximum sample values within each cell. To
do this, one needs to set the number of samples relatively high: the
default is 10,000 and we recommend at least that number. If the model
you are running is a simulation that is computationally quite
expensive, we recommend that you set up a surrogate model within the
Dakota input file so that global_evidence
performs its sampling and
calculations on the surrogate and not on the original model. If one
uses optimization methods instead to find the minimum and maximum
sample values within each cell, this can also be computationally
expensive.
Additional Resources
See the topic page Evidence Theory for important background information and usage notes.
Refer to Variable Support for information on supported variable types.
Theory
The basic idea is that one specifies an “evidence structure” on uncertain inputs and propagates that to obtain belief and plausibility functions on the response functions. The inputs are defined by sets of intervals and Basic Probability Assignments (BPAs). Evidence propagation is computationally expensive, since the minimum and maximum function value must be calculated for each “interval cell combination.” These bounds are aggregated into belief and plausibility.