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Dakota
Version 6.2
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Generates posterior distribution on model parameters given experiment data. More...
Public Member Functions | |
NonDGPMSABayesCalibration (ProblemDescDB &problem_db, Model &model) | |
constructor | |
~NonDGPMSABayesCalibration () | |
destructor | |
Public Attributes | |
int | emulatorSamples |
number of samples of the simulation to construct the GP | |
Real | likelihoodScale |
scale factor for likelihood | |
bool | calibrateSigmaFlag |
flag to indicated if the sigma terms should be calibrated (default true) | |
String | approxImportFile |
name of file from which to import build points to build GP | |
unsigned short | approxImportFormat |
build data import tabular format | |
bool | approxImportActiveOnly |
import active variables only | |
Protected Member Functions | |
void | derived_init_communicators (ParLevLIter pl_iter) |
derived class contributions to initializing the communicators associated with this Iterator instance | |
void | derived_set_communicators (ParLevLIter pl_iter) |
derived class contributions to setting the communicators associated with this Iterator instance | |
void | derived_free_communicators (ParLevLIter pl_iter) |
derived class contributions to freeing the communicators associated with this Iterator instance | |
void | quantify_uncertainty () |
performs a forward uncertainty propagation by using GPM/SA to generate a posterior distribution on parameters given a set of simulation parameter/response data, a set of experimental data, and additional variables to be specified here. | |
Private Attributes | |
Iterator | lhsIter |
LHS iterator for generating samples for GP. | |
Static Private Attributes | |
static NonDGPMSABayesCalibration * | NonDGPMSAInstance |
print the final statistics |
Generates posterior distribution on model parameters given experiment data.
This class provides a wrapper for the functionality provided in the Los Alamos National Laboratory code called GPM/SA (Gaussian Process Models for Simulation Analysis). Although this is a code that provides input/output mapping, it DOES NOT provide the mapping that we usually think of in the NonDeterministic class hierarchy in DAKOTA, where uncertainty in parameter inputs are mapped to uncertainty in simulation responses. Instead, this class takes a pre-existing set of simulation data as well as experimental data, and maps priors on input parameters to posterior distributions on those input parameters, according to a likelihood function. The goal of the MCMC sampling is to produce posterior values of parameter estimates which will produce simulation response values that "match well" to the experimental data. The MCMC is an integral part of the calibration. The data structures in GPM/SA are fairly detailed and nested. Part of this prototyping exercise is to determine what data structures need to be specified and initialized in DAKOTA and sent to GPM/SA, and what data structures will be returned.
NonDGPMSABayesCalibration | ( | ProblemDescDB & | problem_db, |
Model & | model | ||
) |
constructor
This constructor is called for a standard letter-envelope iterator instantiation. In this case, set_db_list_nodes has been called and probDescDB can be queried for settings from the method specification.
References Dakota::abort_handler(), Iterator::assign_rep(), NonDGPMSABayesCalibration::emulatorSamples, ProblemDescDB::get_string(), NonDGPMSABayesCalibration::lhsIter, NonDBayesCalibration::mcmcModel, Iterator::probDescDB, and NonDBayesCalibration::randomSeed.
void quantify_uncertainty | ( | ) | [protected, virtual] |
performs a forward uncertainty propagation by using GPM/SA to generate a posterior distribution on parameters given a set of simulation parameter/response data, a set of experimental data, and additional variables to be specified here.
Perform the uncertainty quantification
Implements NonD.
References Iterator::all_responses(), Analyzer::all_samples(), Iterator::all_samples(), NonDGPMSABayesCalibration::approxImportActiveOnly, NonDGPMSABayesCalibration::approxImportFile, NonDGPMSABayesCalibration::approxImportFormat, NonDGPMSABayesCalibration::calibrateSigmaFlag, ExperimentData::config_vars(), Model::continuous_lower_bounds(), Model::continuous_upper_bounds(), Model::continuous_variables(), NonDGPMSABayesCalibration::emulatorSamples, NonDCalibration::expData, NonDBayesCalibration::initialize_model(), NonDGPMSABayesCalibration::lhsIter, ExperimentData::load_data(), NonDBayesCalibration::mcmcModel, Iterator::methodPCIter, NonD::miPLIndex, NonDGPMSABayesCalibration::NonDGPMSAInstance, NonDCalibration::numExperiments, Analyzer::numFunctions, NonDBayesCalibration::numSamples, NonD::numUncertainVars, Iterator::outputLevel, Iterator::run(), and ExperimentData::scalar_data().
NonDGPMSABayesCalibration * NonDGPMSAInstance [static, private] |
print the final statistics
Pointer to current class instance for use in static callback functions
Referenced by NonDGPMSABayesCalibration::quantify_uncertainty().