Base class for Bayesian inference: generates posterior distribution on model parameters given experimental data. More...
Public Member Functions | |
NonDBayesCalibration (Model &model) | |
standard constructor | |
~NonDBayesCalibration () | |
destructor | |
Protected Member Functions | |
void | quantify_uncertainty () |
performs a forward uncertainty propagation of parameter distributions into response statistics | |
const Model & | algorithm_space_model () const |
return the result of any recasting or surrogate model recursion layered on top of iteratedModel by the derived Iterator ctor chain | |
Protected Attributes | |
Model | emulatorModel |
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) | |
bool | standardizedSpace |
flag indicating use of a variable transformation to standardized probability space | |
Iterator | stochExpIterator |
NonDPolynomialChaos or NonDStochCollocation instance for defining a PCE/SC-based emulatorModel. | |
Private Attributes | |
short | emulatorType |
the emulator type: NO_EMULATOR, GAUSSIAN_PROCESS, POLYNOMIAL_CHAOS, or STOCHASTIC_COLLOCATION |
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 | ( | 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 Iterator::algorithm_space_model(), Iterator::assign_rep(), Model::assign_rep(), NonD::cdfFlag, NonD::construct_lhs(), NonD::construct_u_space_model(), NonDBayesCalibration::emulatorModel, NonDBayesCalibration::emulatorType, ProblemDescDB::get_bool(), ProblemDescDB::get_dusa(), ProblemDescDB::get_int(), ProblemDescDB::get_string(), Iterator::gradientType, Iterator::hessianType, Model::init_communicators(), NonD::initialize_random_variable_correlations(), NonD::initialize_random_variable_transformation(), NonD::initialize_random_variable_types(), Iterator::iteratedModel, Iterator::iterator_rep(), Iterator::probDescDB, NonD::requested_levels(), NonD::respLevelTarget, NonDBayesCalibration::standardizedSpace, NonDBayesCalibration::stochExpIterator, and NonD::verify_correlation_support().