Dakota Reference Manual
Version 6.2
LargeScale Engineering Optimization and Uncertainty Analysis

Gaussian Process Adaptive Importance Sampling
This keyword is related to the topics:
Alias: gaussian_process_adaptive_importance_sampling
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Optional  emulator_samples  Number of data points used to train the surrogate model or emulator  
Optional  import_points_file  File containing variable values and corresponding responses  
Optional  export_points_file  Output file for evaluations of a surrogate model  
Optional  response_levels  Values at which to estimate desired statistics for each response  
Optional  distribution  Selection of cumulative or complementary cumulative functions  
Optional  probability_levels  Specify probability levels at which to estimate the corresponding response value  
Optional  gen_reliability_levels  Specify generalized relability levels at which to estimate the corresponding response value  
Optional  rng  Selection of a random number generator  
Optional  samples  Number of samples for samplingbased methods  
Optional  seed  Seed of the random number generator  
Optional  model_pointer  Identifier for model block to be used by a method 
gpais
is recommended for problems that have a relatively small number of input variables (e.g. less than 1020). This method, Gaussian Process Adaptive Importance Sampling, is outlined in the paperDalbey2012.
This method starts with an initial set of LHS samples and adds samples one at a time, with the goal of adaptively improving the estimate of the ideal importance density during the process. The approach uses a mixture of component densities. An iterative process is used to construct the sequence of improving component densities. At each iteration, a Gaussian process (GP) surrogate is used to help identify areas in the space where failure is likely to occur. The GPs are not used to directly calculate the failure probability; they are only used to approximate the importance density. Thus, the Gaussian process adaptive importance sampling algorithm overcomes limitations involving using a potentially inaccurate surrogate model directly in importance sampling calculations.
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