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Dakota
Version 6.2
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Base class for Bayesian inference: generates posterior distribution on model parameters given experimental data. More...
Public Member Functions | |
NonDBayesCalibration (ProblemDescDB &problem_db, Model &model) | |
standard constructor | |
~NonDBayesCalibration () | |
destructor | |
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 | |
const Model & | algorithm_space_model () const |
void | initialize_model () |
initialize emulator model and probability space transformations | |
Protected Attributes | |
short | emulatorType |
the emulator type: NO_EMULATOR, GP_EMULATOR, PCE_EMULATOR, or SC_EMULATOR | |
Model | mcmcModel |
Model instance employed in the likelihood function; provides response function values from Gaussian processes, stochastic expansions (PCE/SC), or direct access to simulations (no surrogate option) | |
Iterator | stochExpIterator |
NonDPolynomialChaos or NonDStochCollocation instance for defining a PCE/SC-based mcmcModel. | |
int | numSamples |
number of samples in the chain (e.g. number of MCMC samples) | |
int | chainCycles |
number of update cycles for MCMC chain (implemented by restarting of short chains) | |
int | randomSeed |
random seed for MCMC process | |
bool | standardizedSpace |
flag indicating use of a variable transformation to standardized probability space for the model or emulator | |
bool | adaptPosteriorRefine |
flag indicating usage of adaptive posterior refinement; currently makes sense for unstructured grids in GP and PCE least squares/CS | |
String | proposalCovarType |
approach for defining proposal covariance | |
RealVector | proposalCovarData |
data from user input of proposal covariance | |
String | proposalCovarFilename |
filename for user-specified proposal covariance | |
String | proposalCovarInputType |
approach for defining proposal covariance |
Base class for Bayesian inference: generates posterior distribution on model parameters given experimental data.
This class will eventually provide a general-purpose framework for Bayesian inference. In the short term, it only collects shared code between QUESO and GPMSA implementations.
NonDBayesCalibration | ( | ProblemDescDB & | problem_db, |
Model & | model | ||
) |
standard 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 Response::active_set(), NonDBayesCalibration::adaptPosteriorRefine, Iterator::algorithm_space_model(), Iterator::assign_rep(), Model::assign_rep(), NonD::cdfFlag, NonDBayesCalibration::chainCycles, Model::current_response(), NonDBayesCalibration::emulatorType, ProblemDescDB::get_bool(), ProblemDescDB::get_int(), ProblemDescDB::get_real(), ProblemDescDB::get_rv(), ProblemDescDB::get_string(), ProblemDescDB::get_usa(), ProblemDescDB::get_ushort(), Model::gradient_type(), Model::hessian_type(), NonD::initialize_random_variable_correlations(), NonD::initialize_random_variable_transformation(), NonD::initialize_random_variable_types(), Iterator::iteratedModel, Iterator::iterator_rep(), Iterator::maxEvalConcurrency, Iterator::maxIterations, NonDBayesCalibration::mcmcModel, NonDBayesCalibration::numSamples, Iterator::outputLevel, Iterator::probDescDB, NonDBayesCalibration::proposalCovarType, NonDBayesCalibration::randomSeed, ActiveSet::request_values(), NonD::requested_levels(), NonD::respLevelTarget, NonD::respLevelTargetReduce, NonDBayesCalibration::standardizedSpace, NonDBayesCalibration::stochExpIterator, NonD::transform_model(), and NonD::verify_correlation_support().
const Model & algorithm_space_model | ( | ) | const [inline, protected, virtual] |
default definition that gets redefined in selected derived Minimizers
Reimplemented from Analyzer.
References NonDBayesCalibration::mcmcModel.