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NonDGPMSABayesCalibration Class Reference

Generates posterior distribution on model parameters given experiment data. More...

Inheritance diagram for NonDGPMSABayesCalibration:
NonDQUESOBayesCalibration NonDBayesCalibration NonDCalibration NonD Analyzer Iterator

Public Member Functions

 NonDGPMSABayesCalibration (ProblemDescDB &problem_db, Model &model)
 constructor More...
 
 ~NonDGPMSABayesCalibration ()
 destructor
 
- Public Member Functions inherited from NonDQUESOBayesCalibration
 NonDQUESOBayesCalibration (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 ~NonDQUESOBayesCalibration ()
 destructor
 
- Public Member Functions inherited from NonDBayesCalibration
 NonDBayesCalibration (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 ~NonDBayesCalibration ()
 destructor
 
template<typename VectorType >
Real prior_density (const VectorType &vec)
 compute the prior PDF for a particular MCMC sample
 
template<typename VectorType >
Real log_prior_density (const VectorType &vec)
 compute the log prior PDF for a particular MCMC sample
 
template<typename Engine >
void prior_sample (Engine &rng, RealVector &prior_samples)
 draw a multivariate sample from the prior distribution
 
template<typename VectorType >
void prior_mean (VectorType &mean_vec) const
 return the mean of the prior distribution More...
 
template<typename MatrixType >
void prior_variance (MatrixType &var_mat) const
 return the covariance of the prior distribution More...
 
template<>
Real prior_density (const RealVector &vec)
 
template<>
Real log_prior_density (const RealVector &vec)
 
- Public Member Functions inherited from NonDCalibration
 NonDCalibration (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 ~NonDCalibration ()
 destructor
 
bool resize ()
 reinitializes iterator based on new variable size
 
- Public Member Functions inherited from NonD
void requested_levels (const RealVectorArray &req_resp_levels, const RealVectorArray &req_prob_levels, const RealVectorArray &req_rel_levels, const RealVectorArray &req_gen_rel_levels, short resp_lev_tgt, short resp_lev_tgt_reduce, bool cdf_flag, bool pdf_output)
 set requestedRespLevels, requestedProbLevels, requestedRelLevels, requestedGenRelLevels, respLevelTarget, cdfFlag, and pdfOutput (used in combination with alternate ctors)
 
void print_level_mappings (std::ostream &s) const
 prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels for default qoi_type and qoi_labels
 
void print_level_mappings (std::ostream &s, String qoi_type, const StringArray &qoi_labels) const
 prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels More...
 
void print_level_mappings (std::ostream &s, const RealVector &level_maps, bool moment_offset, const String &prepend="")
 print level mapping statistics using optional pre-pend More...
 
bool pdf_output () const
 get pdfOutput
 
void pdf_output (bool output)
 set pdfOutput
 
short final_moments_type () const
 get finalMomentsType
 
void final_moments_type (short type)
 set finalMomentsType
 
- Public Member Functions inherited from Analyzer
const VariablesArray & all_variables ()
 return the complete set of evaluated variables
 
const RealMatrix & all_samples ()
 return the complete set of evaluated samples
 
const IntResponseMap & all_responses () const
 return the complete set of computed responses
 
size_t num_samples () const
 
virtual void vary_pattern (bool pattern_flag)
 sets varyPattern in derived classes that support it
 
- Public Member Functions inherited from Iterator
 Iterator (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 default constructor More...
 
 Iterator (ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 standard envelope constructor, which constructs its own model(s) More...
 
 Iterator (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 alternate envelope constructor which uses the ProblemDescDB but accepts a model from a higher level (meta-iterator) context, instead of constructing its own More...
 
 Iterator (const String &method_string, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 alternate envelope constructor for instantiations by name without the ProblemDescDB More...
 
 Iterator (const Iterator &iterator)
 copy constructor More...
 
virtual ~Iterator ()
 destructor
 
Iterator operator= (const Iterator &iterator)
 assignment operator
 
virtual void post_input ()
 read tabular data for post-run mode
 
virtual void reset ()
 restore initial state for repeated sub-iterator executions
 
virtual void nested_variable_mappings (const SizetArray &c_index1, const SizetArray &di_index1, const SizetArray &ds_index1, const SizetArray &dr_index1, const ShortArray &c_target2, const ShortArray &di_target2, const ShortArray &ds_target2, const ShortArray &dr_target2)
 set primaryA{CV,DIV,DRV}MapIndices, secondaryA{CV,DIV,DRV}MapTargets within derived Iterators; supports computation of higher-level sensitivities in nested contexts (e.g., derivatives of statistics w.r.t. inserted design variables)
 
virtual void nested_response_mappings (const RealMatrix &primary_coeffs, const RealMatrix &secondary_coeffs)
 set primaryResponseCoefficients, secondaryResponseCoefficients within derived Iterators; Necessary for scalarization case in MLMC NonDMultilevelSampling to map scalarization in nested context
 
virtual void initialize_iterator (int job_index)
 used by IteratorScheduler to set the starting data for a run
 
virtual void pack_parameters_buffer (MPIPackBuffer &send_buffer, int job_index)
 used by IteratorScheduler to pack starting data for an iterator run
 
virtual void unpack_parameters_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
 used by IteratorScheduler to unpack starting data for an iterator run
 
virtual void unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer, int job_index)
 used by IteratorScheduler to unpack starting data and initialize an iterator run
 
virtual void pack_results_buffer (MPIPackBuffer &send_buffer, int job_index)
 used by IteratorScheduler to pack results data from an iterator run
 
virtual void unpack_results_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
 used by IteratorScheduler to unpack results data from an iterator run
 
virtual void update_local_results (int job_index)
 used by IteratorScheduler to update local results arrays
 
virtual const RealSymMatrix & response_error_estimates () const
 return error estimates associated with the final iterator solution
 
virtual bool accepts_multiple_points () const
 indicates if this iterator accepts multiple initial points. Default return is false. Override to return true if appropriate.
 
virtual void initial_point (const Variables &pt)
 sets the initial point for this iterator (user-functions mode for which Model updating is not used)
 
virtual void initial_point (const RealVector &pt)
 sets the initial point (active continuous variables) for this iterator (user-functions mode for which Model updating is not used)
 
virtual void initial_points (const VariablesArray &pts)
 sets the multiple initial points for this iterator. This should only be used if accepts_multiple_points() returns true.
 
virtual void variable_bounds (const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds)
 assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)
 
virtual void linear_constraints (const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lb, const RealVector &lin_ineq_ub, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgt)
 assign linear inequality and linear equality constraints for this iterator (user-functions mode for which Model updating is not used)
 
virtual void nonlinear_constraints (const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt)
 assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)
 
virtual void initialize_graphics (int iterator_server_id=1)
 initialize the 2D graphics window and the tabular graphics data More...
 
virtual void check_sub_iterator_conflict ()
 detect any conflicts due to recursive use of the same Fortran solver More...
 
virtual unsigned short uses_method () const
 return name of any enabling iterator used by this iterator
 
virtual void method_recourse ()
 perform a method switch, if possible, due to a detected conflict
 
virtual void sampling_reset (size_t min_samples, bool all_data_flag, bool stats_flag)
 reset sampling iterator to use at least min_samples
 
virtual void sampling_reference (size_t samples_ref)
 set reference number of samples, which is a lower bound during reset
 
virtual void sampling_increment ()
 increment to next in sequence of refinement samples
 
virtual void random_seed (int seed)
 set randomSeed, if present
 
virtual unsigned short sampling_scheme () const
 return sampling name
 
virtual IntIntPair estimate_partition_bounds ()
 estimate the minimum and maximum partition sizes that can be utilized by this Iterator
 
virtual void declare_sources ()
 Declare sources to the evaluations database.
 
void init_communicators (ParLevLIter pl_iter)
 initialize the communicators associated with this Iterator instance
 
void set_communicators (ParLevLIter pl_iter)
 set the communicators associated with this Iterator instance
 
void free_communicators (ParLevLIter pl_iter)
 free the communicators associated with this Iterator instance
 
void resize_communicators (ParLevLIter pl_iter, bool reinit_comms)
 Resize the communicators. This is called from the letter's resize()
 
void parallel_configuration_iterator (ParConfigLIter pc_iter)
 set methodPCIter
 
ParConfigLIter parallel_configuration_iterator () const
 return methodPCIter
 
void parallel_configuration_iterator_map (std::map< size_t, ParConfigLIter > pci_map)
 set methodPCIterMap
 
std::map< size_t, ParConfigLIter > parallel_configuration_iterator_map () const
 return methodPCIterMap
 
void run (ParLevLIter pl_iter)
 invoke set_communicators(pl_iter) prior to run()
 
void run ()
 orchestrate initialize/pre/core/post/finalize phases More...
 
void assign_rep (std::shared_ptr< Iterator > iterator_rep)
 replaces existing letter with a new one More...
 
void iterated_model (const Model &model)
 set the iteratedModel (iterators and meta-iterators using a single model instance)
 
Modeliterated_model ()
 return the iteratedModel (iterators & meta-iterators using a single model instance)
 
ProblemDescDBproblem_description_db () const
 return the problem description database (probDescDB)
 
ParallelLibraryparallel_library () const
 return the parallel library (parallelLib)
 
void method_name (unsigned short m_name)
 set the method name to an enumeration value
 
unsigned short method_name () const
 return the method name via its native enumeration value
 
void method_string (const String &m_str)
 set the method name by string
 
String method_string () const
 return the method name by string
 
String method_enum_to_string (unsigned short method_enum) const
 convert a method name enumeration value to a string
 
unsigned short method_string_to_enum (const String &method_str) const
 convert a method name string to an enumeration value
 
String submethod_enum_to_string (unsigned short submethod_enum) const
 convert a sub-method name enumeration value to a string
 
const String & method_id () const
 return the method identifier (methodId)
 
int maximum_evaluation_concurrency () const
 return the maximum evaluation concurrency supported by the iterator
 
void maximum_evaluation_concurrency (int max_conc)
 set the maximum evaluation concurrency supported by the iterator
 
size_t maximum_iterations () const
 return the maximum iterations for this iterator
 
void maximum_iterations (size_t max_iter)
 set the maximum iterations for this iterator
 
void convergence_tolerance (Real conv_tol)
 set the method convergence tolerance (convergenceTol)
 
Real convergence_tolerance () const
 return the method convergence tolerance (convergenceTol)
 
void output_level (short out_lev)
 set the method output level (outputLevel)
 
short output_level () const
 return the method output level (outputLevel)
 
void summary_output (bool summary_output_flag)
 Set summary output control; true enables evaluation/results summary.
 
size_t num_final_solutions () const
 return the number of solutions to retain in best variables/response arrays
 
void num_final_solutions (size_t num_final)
 set the number of solutions to retain in best variables/response arrays
 
void active_set (const ActiveSet &set)
 set the default active set (for use with iterators that employ evaluate_parameter_sets())
 
const ActiveSetactive_set () const
 return the default active set (used by iterators that employ evaluate_parameter_sets())
 
void active_set_request_vector (const ShortArray &asv)
 return the default active set request vector (used by iterators that employ evaluate_parameter_sets())
 
const ShortArray & active_set_request_vector () const
 return the default active set request vector (used by iterators that employ evaluate_parameter_sets())
 
void active_set_request_values (short asv_val)
 return the default active set request vector (used by iterators that employ evaluate_parameter_sets())
 
void sub_iterator_flag (bool si_flag)
 set subIteratorFlag (and update summaryOutputFlag if needed)
 
bool is_null () const
 function to check iteratorRep (does this envelope contain a letter?)
 
std::shared_ptr< Iteratoriterator_rep () const
 returns iteratorRep for access to derived class member functions that are not mapped to the top Iterator level
 
virtual void eval_tag_prefix (const String &eval_id_str)
 set the hierarchical eval ID tag prefix More...
 
std::shared_ptr< TraitsBasetraits () const
 returns methodTraits for access to derived class member functions that are not mapped to the top TraitsBase level
 
bool top_level ()
 Return whether the iterator is the top level iterator.
 
void top_level (bool tflag)
 Set the iterator's top level flag.
 

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 calibrate ()
 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. More...
 
void init_queso_solver ()
 specialization to initialize the inverse problem and posterior
 
void acquire_simulation_data (RealMatrix &sim_data)
 Populate simulation data (run design of experiments or load build data)
 
void overlay_proposal_covariance (QUESO::GslMatrix &full_prop_cov) const
 fill the full proposal covariance, inlcuding hyperparameter entries with user-specified or default theta covariance information
 
void fill_simulation_data ()
 populate the simulation data, calculating and applying scaling if needed More...
 
void fill_experiment_data ()
 populate the experiment data, applying scaling if needed
 
void overlay_initial_params (QUESO::GslVector &full_param_initials)
 overlay the Dakota user's initial parameters on the full GPMSA vector of parameters
 
void cache_acceptance_chain ()
 retrieve the chain from QUESO and populate acceptanceChain / acceptedFnVals More...
 
void print_results (std::ostream &s, short results_state=FINAL_RESULTS)
 print the final iterator results More...
 
- Protected Member Functions inherited from NonDQUESOBayesCalibration
void calibrate ()
 
void print_results (std::ostream &s, short results_state=FINAL_RESULTS) override
 print the final iterator results More...
 
void init_queso_environment ()
 initialize the QUESO FullEnvironment on the Dakota MPIComm
 
void init_precond_request_value ()
 initialize the ASV value for preconditioned cases
 
void specify_prior () override
 intialize the QUESO parameter space, min, max, initial, domain, and prior define solver options, likelihood callback, posterior RV, and inverse problem More...
 
void specify_likelihood () override
 
void init_bayesian_solver () override
 
void specify_posterior () override
 
void precondition_proposal (unsigned int chain_index)
 use derivative information from the emulator to define the proposal covariance (inverse of misfit Hessian)
 
void run_queso_solver ()
 perform the MCMC process
 
void map_pre_solve () override
 
void run_chain ()
 short term option to restart the MCMC chain with updated proposal density computed from the emulator at a new starting point
 
void cache_chain ()
 cache the chain to acceptanceChain and acceptedFnVals More...
 
void log_best ()
 log at most batchSize best chain points into bestSamples
 
void filter_chain_by_conditioning () override
 extract batchSize points from the MCMC chain and store final aggregated set within allSamples; unique points with best conditioning are selected, as determined by pivoted LU
 
void init_proposal_covariance ()
 
void prior_proposal_covariance ()
 use covariance of prior distribution for setting proposal covariance More...
 
void user_proposal_covariance (const String &input_fmt, const RealVector &cov_data, const String &cov_filename)
 Set proposal covariance from user-provided diagonal or matrix. More...
 
void validate_proposal ()
 
void set_ip_options ()
 Set inverse problem options calIpOptionsValues common to all solvers. More...
 
void set_mh_options ()
 Set MH-specific inverse problem options calIpMhOptionsValues.
 
void update_chain_size (unsigned int size)
 update MH-specific inverse problem options calIpMhOptionsValues
 
void copy_gsl (const QUESO::GslVector &qv, RealVector &rv)
 local copy_data utility from GslVector to RealVector
 
void copy_gsl (const RealVector &rv, QUESO::GslVector &qv)
 local copy_data utility from RealVector to GslVector
 
void copy_gsl_partial (const QUESO::GslVector &qv, size_t start, RealVector &rv)
 local copy_data utility from portion of GslVector to RealVector
 
void copy_gsl_partial (const RealVector &rv, QUESO::GslVector &qv, size_t start)
 local copy_data utility from RealVector to portion of GslVector
 
void copy_gsl (const QUESO::GslVector &qv, RealMatrix &rm, int i)
 local copy_data utility from GslVector to column in RealMatrix
 
bool equal_gsl (const QUESO::GslVector &qv1, const QUESO::GslVector &qv2)
 equality tester for two GslVectors
 
- Protected Member Functions inherited from NonDBayesCalibration
void pre_run ()
 pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...
 
void core_run ()
 core portion of run; implemented by all derived classes and may include pre/post steps in lieu of separate pre/post More...
 
void print_variables (std::ostream &s, const RealVector &c_vars)
 convenience function to print calibration parameters, e.g., for MAP / best parameters
 
const Modelalgorithm_space_model () const
 
void construct_mcmc_model ()
 construct mcmcModel (no emulation, GP, PCE, or SC) that wraps inbound Model
 
void init_hyper_parameters ()
 initialize the hyper-parameter priors
 
void init_map_optimizer ()
 initialize the MAP optimizer selection More...
 
void construct_map_model ()
 construct the negative log posterior RecastModel (negLogPostModel)
 
void construct_map_optimizer ()
 construct the MAP optimizer for minimization of negLogPostModel
 
void initialize_model ()
 initialize emulator model and probability space transformations
 
void calibrate_with_adaptive_emulator ()
 Run calibration, looping to refine emulator around posterior mode. More...
 
void best_to_all ()
 copy bestSamples to allSamples to use in surrogate update
 
void update_model ()
 evaluates allSamples on iteratedModel and update the mcmcModel emulator with all{Samples,Responses}
 
Real assess_emulator_convergence ()
 compute the L2 norm of the change in emulator coefficients
 
void calibrate_to_hifi ()
 calibrate the model to a high-fidelity data source, using mutual information-guided design of experiments (adaptive experimental design)
 
void eval_hi2lo_stop (bool &stop_metric, double &prev_MI, const RealVector &MI_vec, int num_hifi, int max_hifi, int num_candidates)
 evaluate stopping criteria for calibrate_to_hifi
 
void print_hi2lo_file (std::ostream &out_file, int num_it, const VariablesArray &optimal_config_matrix, const RealVector &MI_vec, RealMatrix &resp_matrix)
 print calibrate_to_hifi progress to file
 
void print_hi2lo_begin (int num_it)
 print calibrate_to_hifi progress
 
void print_hi2lo_status (int num_it, int i, const Variables &xi_i, double MI)
 
void print_hi2lo_batch_status (int num_it, int batch_n, int batchEvals, const Variables &optimal_config, double max_MI)
 
void print_hi2lo_selected (int num_it, const VariablesArray &optimal_config_matrix, const RealVector &MI_vec)
 
void print_hi2lo_chain_moments ()
 
void add_lhs_hifi_data ()
 supplement high-fidelity data with LHS samples
 
void choose_batch_from_mutual_info (int random_seed, int num_it, int max_hifi, int num_hifi, RealMatrix &mi_chain, VariablesArray &design_matrix, VariablesArray &optimal_config_matrix, RealVector &MI_vec)
 calculate the optimal points to add for a given batch
 
void apply_hifi_sim_error (int &random_seed, int num_exp, int exp_offset=0)
 apply simulation error vector
 
void apply_error_vec (const RealVector &error_vec, int &seed, int experiment)
 
void build_error_matrix (const RealVector &sim_error_vec, RealMatrix &sim_error_matrix, int &seed)
 build matrix of errors
 
void build_designs (VariablesArray &design_matrix)
 build matrix of candidate points More...
 
void build_hi2lo_xmatrix (RealMatrix &Xmatrix, int i, const RealMatrix &mi_chain, RealMatrix &sim_error_matrix)
 build matrix to calculate mutual information for calibrate_to_hifi
 
void run_hifi (const VariablesArray &optimal_config_matrix, RealMatrix &resp_matrix)
 run high-fidelity model at several configs and add to experiment data
 
void build_model_discrepancy ()
 calculate model discrepancy with respect to experimental data
 
void build_scalar_discrepancy ()
 
void build_field_discrepancy ()
 
void build_GP_field (const RealMatrix &t, RealMatrix &t_pred, const RealVector &concat_disc, RealVector &disc_pred, RealVector &disc_var)
 
void calculate_kde ()
 calculate a Kernel Density Estimate (KDE) for the posterior samples
 
void calculate_evidence ()
 calculate the model evidence
 
void extract_selected_posterior_samples (const std::vector< int > &points_to_keep, const RealMatrix &samples_for_posterior_eval, const RealVector &posterior_density, RealMatrix &posterior_data) const
 
void export_posterior_samples_to_file (const std::string filename, const RealMatrix &posterior_data) const
 
template<typename VectorType1 , typename VectorType2 >
void augment_gradient_with_log_prior (VectorType1 &log_grad, const VectorType2 &vec)
 compute the (approximate) gradient of the negative log posterior by augmenting the (approximate) gradient of the negative log likelihood with the gradient of the negative log prior
 
template<typename MatrixType , typename VectorType >
void augment_hessian_with_log_prior (MatrixType &log_hess, const VectorType &vec)
 compute the (approximate) Hessian of the negative log posterior by augmenting the (approximate) Hessian of the negative log likelihood with the Hessian of the negative log prior
 
Real log_likelihood (const RealVector &residuals, const RealVector &hyper_params)
 calculate log-likelihood from the passed residuals (assuming they are already sized and scaled by covariance / hyperparams... More...
 
void prior_cholesky_factorization ()
 compute priorCovCholFactor based on prior distributions for random variables and any hyperparameters
 
void get_positive_definite_covariance_from_hessian (const RealSymMatrix &hessian, RealSymMatrix &covariance)
 member version forwards member data to static function
 
void scale_model ()
 Wrap iteratedModel in a RecastModel that performs response scaling. More...
 
void weight_model ()
 Wrap iteratedModel in a RecastModel that weights the residuals. More...
 
void export_discrepancy (RealMatrix &pred_config_mat)
 print tabular files containing model+discrepancy responses and variances
 
void export_field_discrepancy (RealMatrix &pred_vars_mat)
 print tabular files containing model+discrepancy responses and variances for field responses
 
virtual void compute_statistics ()
 Compute final stats for MCMC chains.
 
void export_chain ()
 export the acceptance chain in user space
 
void export_chain (RealMatrix &filtered_chain, RealMatrix &filtered_fn_vals)
 Print filtered posterior and function values (later: credibility and prediction intervals) More...
 
void filter_chain (const RealMatrix &acceptance_chain, RealMatrix &filtered_chain, int target_length)
 Perform chain filtering based on target chain length.
 
void filter_chain (const RealMatrix &acceptance_chain, RealMatrix &filtered_chain)
 Perform chain filtering with burn-in and sub-sampling.
 
void filter_fnvals (const RealMatrix &accepted_fn_vals, RealMatrix &filtered_fn_vals)
 
void filter_matrix_cols (const RealMatrix &orig_matrix, int start_index, int stride, RealMatrix &filtered_matrix)
 return a newly allocated filtered matrix including start_index and every stride-th index after; for burn-in cases, start_index is the number of burn-in discards
 
void compute_intervals ()
 
void compute_prediction_vals (RealMatrix &filtered_fn_vals, RealMatrix &PredVals, int num_filtered, size_t num_exp, size_t num_concatenated)
 
void print_intervals_file (std::ostream &stream, RealMatrix &functionvalsT, RealMatrix &predvalsT, int length, size_t aug_length)
 
void print_intervals_screen (std::ostream &stream, RealMatrix &functionvalsT, RealMatrix &predvalsT, int length)
 
void kl_post_prior (RealMatrix &acceptanceChain)
 Compute information metrics.
 
void prior_sample_matrix (RealMatrix &prior_dist_samples)
 
void mutual_info_buildX ()
 
void print_kl (std::ostream &stream)
 
void print_chain_diagnostics (std::ostream &s)
 
void print_batch_means_intervals (std::ostream &s)
 
- Protected Member Functions inherited from NonD
 NonD (ProblemDescDB &problem_db, Model &model)
 constructor
 
 NonD (unsigned short method_name, Model &model)
 alternate constructor for sample generation and evaluation "on the fly"
 
 NonD (unsigned short method_name, Model &model, const ShortShortPair &approx_view)
 alternate constructor for sample generation and evaluation "on the fly"
 
 NonD (unsigned short method_name, const RealVector &lower_bnds, const RealVector &upper_bnds)
 alternate constructor for sample generation "on the fly"
 
 ~NonD ()
 destructor
 
void initialize_run ()
 utility function to perform common operations prior to pre_run(); typically memory initialization; setting of instance pointers More...
 
void finalize_run ()
 utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More...
 
const Responseresponse_results () const
 return the final statistics from the nondeterministic iteration
 
void response_results_active_set (const ActiveSet &set)
 set the active set within finalStatistics
 
virtual void initialize_response_covariance ()
 initializes respCovariance
 
virtual void initialize_final_statistics ()
 initializes finalStatistics for storing NonD final results More...
 
virtual void update_final_statistics ()
 update finalStatistics::functionValues
 
virtual bool discrepancy_sample_counts () const
 flag identifying whether sample counts correspond to level discrepancies
 
void pull_level_mappings (RealVector &level_maps, size_t offset)
 concatenate computed{Resp,Prob,Rel,GenRel}Levels into level_maps
 
void push_level_mappings (const RealVector &level_maps, size_t offset)
 update computed{Resp,Prob,Rel,GenRel}Levels from level_maps
 
void configure_sequence (size_t &num_steps, size_t &secondary_index, short &seq_type)
 configure fidelity/level counts from model hierarchy More...
 
void configure_cost (unsigned short num_steps, bool multilevel, RealVector &cost)
 extract cost estimates from model hierarchy (forms or resolutions)
 
bool query_cost (unsigned short num_steps, bool multilevel, RealVector &cost)
 extract cost estimates from model hierarchy, if available
 
bool query_cost (unsigned short num_steps, Model &model, RealVector &cost)
 extract cost estimates from model hierarchy, if available
 
bool valid_cost_values (const RealVector &cost)
 test cost for valid values > 0
 
void load_pilot_sample (const SizetArray &pilot_spec, size_t num_steps, SizetArray &delta_N_l)
 distribute pilot sample specification across model forms or levels
 
void load_pilot_sample (const SizetArray &pilot_spec, short seq_type, const Sizet3DArray &N_l, Sizet2DArray &delta_N_l)
 distribute pilot sample specification across model forms and levels
 
template<typename ArrayType >
void inflate_approx_samples (const ArrayType &N_l, bool multilev, size_t secondary_index, std::vector< ArrayType > &N_l_vec)
 update the relevant slice of N_l_3D from the final 2D multilevel or 2D multifidelity sample profile
 
template<typename ArrayType >
void inflate_sequence_samples (const ArrayType &N_l, bool multilev, size_t secondary_index, std::vector< ArrayType > &N_l_vec)
 update the relevant slice of N_l_3D from the final 2D multilevel or 2D multifidelity sample profile
 
void resize_final_statistics_gradients ()
 resizes finalStatistics::functionGradients based on finalStatistics ASV
 
void update_aleatory_final_statistics ()
 update finalStatistics::functionValues from momentStats and computed{Prob,Rel,GenRel,Resp}Levels
 
void update_system_final_statistics ()
 update system metrics from component metrics within finalStatistics
 
void update_system_final_statistics_gradients ()
 update finalStatistics::functionGradients
 
void initialize_level_mappings ()
 size computed{Resp,Prob,Rel,GenRel}Levels
 
void compute_densities (const RealRealPairArray &min_max_fns, bool prob_refinement=false, bool all_levels_computed=false)
 compute the PDF bins from the CDF/CCDF values and store in computedPDF{Abscissas,Ordinates} More...
 
void print_densities (std::ostream &s) const
 output the PDFs reflected in computedPDF{Abscissas,Ordinates} using default qoi_type and pdf_labels
 
void print_densities (std::ostream &s, String qoi_type, const StringArray &pdf_labels) const
 output the PDFs reflected in computedPDF{Abscissas,Ordinates}
 
void print_system_mappings (std::ostream &s) const
 print system series/parallel mappings for response levels
 
void print_multilevel_evaluation_summary (std::ostream &s, const SizetArray &N_m)
 print evaluation summary for multilevel sampling across 1D level profile
 
void print_multilevel_evaluation_summary (std::ostream &s, const Sizet2DArray &N_m)
 print evaluation summary for multilevel sampling across 2D level+QoI profile
 
void print_multilevel_discrepancy_summary (std::ostream &s, const SizetArray &N_m)
 print evaluation summary for multilevel sampling across 1D level profile for discrepancy across levels
 
void print_multilevel_discrepancy_summary (std::ostream &s, const SizetArray &N_m, const SizetArray &N_mp1)
 print evaluation summary for multilevel sampling across 1D level profile for discrepancy across model forms
 
void print_multilevel_discrepancy_summary (std::ostream &s, const Sizet2DArray &N_m)
 print evaluation summary for multilevel sampling across 2D level+QoI profile for discrepancy across levels
 
void print_multilevel_discrepancy_summary (std::ostream &s, const Sizet2DArray &N_m, const Sizet2DArray &N_mp1)
 print evaluation summary for multilevel sampling across 2D level+QoI profile for discrepancy across model forms
 
template<typename ArrayType >
void print_multilevel_model_summary (std::ostream &s, const std::vector< ArrayType > &N_samp, String type, short seq_type, bool discrep_flag)
 print evaluation summary for multilevel sampling across 2D model+level profile (allocations) or 3D model+level+QoI profile (actual)
 
void construct_lhs (Iterator &u_space_sampler, Model &u_model, unsigned short sample_type, int num_samples, int seed, const String &rng, bool vary_pattern, short sampling_vars_mode=ACTIVE)
 assign a NonDLHSSampling instance within u_space_sampler
 
unsigned short sub_optimizer_select (unsigned short requested_sub_method, unsigned short default_sub_method=SUBMETHOD_NPSOL)
 utility for vetting sub-method request against optimizers within the package configuration
 
size_t one_sided_delta (Real current, Real target)
 compute a one-sided sample increment for multilevel methods to move current sampling level to a new target
 
size_t one_sided_delta (const SizetArray &current, const RealVector &targets, size_t power)
 compute a one-sided sample increment for multilevel methods to move current sampling level to a new target
 
size_t one_sided_delta (const SizetArray &current, Real target, size_t power)
 compute a one-sided sample increment for multilevel methods to move current sampling level to a new target
 
bool differ (size_t N_alloc_ij, const SizetArray &N_actual_ij) const
 return true if fine-grained reporting differs from coarse-grained
 
bool differ (const SizetArray &N_alloc_i, const Sizet2DArray &N_actual_i) const
 return true if fine-grained reporting differs from coarse-grained
 
bool differ (const Sizet2DArray &N_alloc, const Sizet3DArray &N_actual) const
 return true if fine-grained reporting differs from coarse-grained
 
void archive_allocate_mappings ()
 allocate results array storage for distribution mappings
 
void archive_from_resp (size_t fn_index, size_t inc_id=0)
 archive the mappings from specified response levels for specified fn
 
void archive_to_resp (size_t fn_index, size_t inc_id=0)
 archive the mappings to computed response levels for specified fn and (optional) increment id.
 
void archive_allocate_pdf ()
 allocate results array storage for pdf histograms
 
void archive_pdf (size_t fn_index, size_t inc_id=0)
 archive a single pdf histogram for specified function
 
void archive_equiv_hf_evals (const Real equiv_hf_evals)
 archive the equivalent number of HF evals (used by ML/MF methods)
 
- Protected Member Functions inherited from Analyzer
 Analyzer ()
 default constructor
 
 Analyzer (ProblemDescDB &problem_db, Model &model)
 standard constructor
 
 Analyzer (unsigned short method_name, Model &model)
 alternate constructor for instantiations "on the fly" with a Model
 
 Analyzer (unsigned short method_name, Model &model, const ShortShortPair &view_override)
 alternate constructor for instantiations "on the fly" with a Model
 
 Analyzer (unsigned short method_name)
 alternate constructor for instantiations "on the fly" without a Model
 
 ~Analyzer ()
 destructor
 
virtual void get_parameter_sets (Model &model)
 Generate one block of numSamples samples (ndim * num_samples), populating allSamples; ParamStudy is the only class that specializes to use allVariables.
 
virtual void get_parameter_sets (Model &model, const size_t num_samples, RealMatrix &design_matrix)
 Generate one block of numSamples samples (ndim * num_samples), populating design_matrix.
 
virtual void update_model_from_sample (Model &model, const Real *sample_vars)
 update model's current variables with data from sample
 
virtual void update_model_from_variables (Model &model, const Variables &vars)
 update model's current variables with data from vars
 
virtual void sample_to_variables (const Real *sample_vars, Variables &vars)
 convert column of samples array to variables; derived classes may reimplement for more than active continuous variables More...
 
void update_from_model (const Model &model)
 set inherited data attributes based on extractions from incoming model
 
void post_run (std::ostream &s)
 post-run portion of run (optional); verbose to print results; re-implemented by Iterators that can read all Variables/Responses and perform final analysis phase in a standalone way More...
 
void pre_output ()
 
const Variablesvariables_results () const
 return a single final iterator solution (variables)
 
const VariablesArray & variables_array_results ()
 return multiple final iterator solutions (variables). This should only be used if returns_multiple_points() returns true.
 
const ResponseArray & response_array_results ()
 return multiple final iterator solutions (response). This should only be used if returns_multiple_points() returns true.
 
bool compact_mode () const
 returns Analyzer::compactMode
 
bool returns_multiple_points () const
 indicates if this iterator returns multiple final points. Default return is false. Override to return true if appropriate.
 
void evaluate_parameter_sets (Model &model, bool log_resp_flag, bool log_best_flag)
 perform function evaluations to map parameter sets (allVariables) into response sets (allResponses) More...
 
void get_vbd_parameter_sets (Model &model, size_t num_samples)
 generate replicate parameter sets for use in variance-based decomposition More...
 
void compute_vbd_stats (const size_t num_samples, const IntResponseMap &resp_samples)
 compute VBD-based Sobol indices More...
 
void archive_sobol_indices () const
 archive VBD-based Sobol indices More...
 
virtual void archive_model_variables (const Model &, size_t idx) const
 archive model evaluation points
 
virtual void archive_model_response (const Response &, size_t idx) const
 archive model evaluation responses
 
void read_variables_responses (int num_evals, size_t num_vars)
 convenience function for reading variables/responses (used in derived classes post_input) More...
 
void print_sobol_indices (std::ostream &s) const
 Printing of VBD results. More...
 
void samples_to_variables_array (const RealMatrix &sample_matrix, VariablesArray &vars_array)
 convert samples array to variables array; e.g., allSamples to allVariables
 
virtual void variables_to_sample (const Variables &vars, Real *sample_c_vars)
 convert the active continuous variables into a column of allSamples More...
 
void variables_array_to_samples (const VariablesArray &vars_array, RealMatrix &sample_matrix)
 convert variables array to samples array; e.g., allVariables to allSamples
 
- Protected Member Functions inherited from Iterator
 Iterator (BaseConstructor, ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More...
 
 Iterator (NoDBBaseConstructor, unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 alternate constructor for base iterator classes constructed on the fly More...
 
 Iterator (NoDBBaseConstructor, unsigned short method_name, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 alternate constructor for base iterator classes constructed on the fly More...
 
 Iterator (NoDBBaseConstructor, Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
 alternate envelope constructor for instantiations without ProblemDescDB More...
 
virtual const VariablesArray & initial_points () const
 gets the multiple initial points for this iterator. This will only be meaningful after a call to initial_points mutator.
 
StrStrSizet run_identifier () const
 get the unique run identifier based on method name, id, and number of executions
 
void initialize_model_graphics (Model &model, int iterator_server_id)
 helper function that encapsulates initialization operations, modular on incoming Model instance More...
 
void export_final_surrogates (Model &data_fit_surr_model)
 export final surrogates generated, e.g., GP in EGO and friends More...
 

Protected Attributes

int buildSamples
 number of samples of the simulation to construct the GP
 
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
 
unsigned int userConfigVars
 number of user-specified configuration (scenario) vars
 
unsigned int gpmsaConfigVars
 number of config vars presented to GPMSA (minimum 1)
 
bool gpmsaNormalize
 whether to apply GPMSA-internal variable and data normalization
 
std::shared_ptr< QUESO::VectorSpace< QUESO::GslVector, QUESO::GslMatrix > > configSpace
 vector space defining the scenario (configuration) variables
 
std::shared_ptr< QUESO::VectorSpace< QUESO::GslVector, QUESO::GslMatrix > > nEtaSpace
 vector space defining the output (response) space for the simulations
 
std::shared_ptr< QUESO::VectorSpace< QUESO::GslVector, QUESO::GslMatrix > > experimentSpace
 vector space defining the output (response) space for the experiments
 
std::shared_ptr< QUESO::GPMSAOptions > gpmsaOptions
 Configuration options for the GPMSA solver.
 
std::shared_ptr< QUESO::GPMSAFactory< QUESO::GslVector, QUESO::GslMatrix > > gpmsaFactory
 core factory that manages a GP-based likelihood
 
- Protected Attributes inherited from NonDQUESOBayesCalibration
String mcmcType
 MCMC type ("dram" or "delayed_rejection" or "adaptive_metropolis" or "metropolis_hastings" or "multilevel", within QUESO)
 
int propCovUpdatePeriod
 period (number of accepted chain samples) for proposal covariance update
 
short precondRequestValue
 the active set request value to use in proposal preconditioning
 
bool logitTransform
 flag indicating user activation of logit transform option More...
 
std::shared_ptr< QUESO::EnvOptionsValues > envOptionsValues
 options for setting up the QUESO Environment
 
std::shared_ptr< QUESO::FullEnvironment > quesoEnv
 top-level QUESO Environment
 
std::shared_ptr< QUESO::VectorSpace< QUESO::GslVector, QUESO::GslMatrix > > paramSpace
 QUESO parameter space based on number of calibrated parameters.
 
std::shared_ptr< QUESO::BoxSubset< QUESO::GslVector, QUESO::GslMatrix > > paramDomain
 QUESO parameter domain: hypercube based on min/max values.
 
std::shared_ptr< QUESO::GslVector > paramInitials
 initial parameter values at which to start chain
 
std::shared_ptr< QUESO::BaseVectorRV< QUESO::GslVector, QUESO::GslMatrix > > priorRv
 random variable for the prior
 
std::shared_ptr< QUESO::GslMatrix > proposalCovMatrix
 proposal covariance for DRAM
 
double priorPropCovMult
 optional multiplier to scale prior-based proposal covariance
 
std::shared_ptr< QUESO::SipOptionsValues > calIpOptionsValues
 general inverse problem options
 
std::shared_ptr< QUESO::MhOptionsValues > calIpMhOptionsValues
 MH-specific inverse problem options.
 
std::shared_ptr< QUESO::GenericScalarFunction< QUESO::GslVector, QUESO::GslMatrix > > likelihoodFunctionObj
 
std::shared_ptr< QUESO::GenericVectorRV< QUESO::GslVector, QUESO::GslMatrix > > postRv
 random variable for the posterior
 
std::shared_ptr< QUESO::StatisticalInverseProblem< QUESO::GslVector, QUESO::GslMatrix > > inverseProb
 QUESO inverse problem solver.
 
String advancedOptionsFile
 advanced options file name (GPMSA only); settings from this file override any C++ / Dakota input file settings
 
- Protected Attributes inherited from NonDBayesCalibration
String scalarDataFilename
 
short emulatorType
 the emulator type: NO_EMULATOR, GP_EMULATOR, PCE_EMULATOR, SC_EMULATOR, ML_PCE_EMULATOR, MF_PCE_EMULATOR, or MF_SC_EMULATOR
 
RealVectorArray prevCoeffs
 cache previous expansion coefficients for assessing convergence of emulator refinement process
 
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)
 
bool mcmcModelHasSurrogate
 whether the MCMC Model is a surrogate, or a thin transformation around a surrogate, so can be cheaply re-evaluated in chain recovery
 
Model residualModel
 DataTransformModel wrapping the mcmcModel.
 
Iterator mapOptimizer
 SQP or NIP optimizer for pre-solving for the MAP point prior to MCMC. This is restricted to emulator cases for now, but as for derivative preconditioning, could be activated for no-emulator cases with a specification option (not active by default).
 
Model negLogPostModel
 RecastModel for solving for MAP: reduces residualModel to scalar negative log posterior.
 
unsigned short mapOptAlgOverride
 user setting of the MAP optimization algorithm type
 
Iterator stochExpIterator
 NonDPolynomialChaos or NonDStochCollocation instance for defining a PCE/SC-based mcmcModel.
 
int chainSamples
 number of samples in the chain (e.g. number of MCMC samples); for iterative update cycles, number of samples per update cycle
 
int randomSeed
 random seed for MCMC process
 
unsigned int batchSize
 number of points to add to surrogate at each iteration of calibrate_with_adaptive_emulator
 
short mcmcDerivOrder
 order of derivatives used in MCMC process (bitwise like ASV)
 
RealVector mapSoln
 solution for most recent MAP pre-solve; also serves as initial guess for initializing the first solve and warm-starting the next solve (posterior emulator refinement)
 
bool adaptExpDesign
 whether to perform iterative design of experiments with high-fidelity model
 
size_t numCandidates
 number of candidate designs for adaptive Bayesian experimental design
 
String importCandPtsFile
 whether to import candidate design points for adaptive Bayesian experimental design
 
unsigned short importCandFormat
 tabular format for the candidate design points import file
 
int maxHifiEvals
 maximum number of high-fidelity model runs to be used for adaptive Bayesian experimental design
 
int batchEvals
 number of optimal designs selected per iteration of experimental design algorithm
 
unsigned short mutualInfoAlg
 algorithm to employ in calculating mutual information
 
bool readFieldCoords
 need field coordinates for model discrepancy
 
bool calModelDiscrepancy
 flag whether to calculate model discrepancy
 
String discrepancyType
 set discrepancy type
 
String exportCorrModelFile
 filename for corrected model (model+discrepancy) calculations output
 
String exportDiscrepFile
 filename for discrepancy calculations output
 
String exportCorrVarFile
 filename for corrected model variance calculations
 
unsigned short exportCorrModelFormat
 format options for corrected model output
 
unsigned short exportDiscrepFormat
 format options for discrepancy output
 
unsigned short exportCorrVarFormat
 format options for corrected model variance output
 
short discrepPolyOrder
 specify polynomial or trend order for discrepancy correction
 
size_t numPredConfigs
 number of prediction configurations at which to calculate model discrepancy
 
RealVector configLowerBnds
 lower bounds on configuration domain
 
RealVector configUpperBnds
 upper bounds on configuration domain
 
ResponseArray discrepancyResponses
 array containing predicted of model+discrepancy
 
ResponseArray correctedResponses
 array containing predicted of model+discrepancy
 
RealMatrix correctedVariances
 matrix containing variances of model+discrepancy
 
RealVector predictionConfigList
 list of prediction configurations at which to calculate model discrepancy
 
String importPredConfigs
 whether to import prediction configurations at which to calculate model discrepancy
 
unsigned short importPredConfigFormat
 tabular format for prediction configurations import file
 
RealVector discrepancyFieldResponses
 array containing predicted of model+discrepancy
 
RealVector correctedFieldResponses
 array containing predicted of model+discrepancy
 
RealVector correctedFieldVariances
 matrix containing variances of model+discrepancy
 
Model hifiModel
 a high-fidelity model data source (given by pointer in input)
 
int initHifiSamples
 initial high-fidelity model samples
 
Iterator hifiSampler
 LHS iterator to generate hi-fi model data.
 
RealMatrix priorCovCholFactor
 the Cholesky factor of the prior covariance
 
unsigned short obsErrorMultiplierMode
 mode for number of observation error multipliers to calibrate (default none)
 
int numHyperparams
 calculated number of hyperparameters augmenting the calibration parameter set, e.g., due to calibrate observation error multipliers
 
RealVector invGammaAlphas
 alphas for inverse gamma distribution on hyper-params
 
RealVector invGammaBetas
 alphas for inverse gamma distribution on hyper-params
 
std::vector< Pecos::RandomVariable > invGammaDists
 distributions for hyper-params
 
bool standardizedSpace
 flag indicating use of a variable transformation to standardized probability space for the model or emulator
 
bool posteriorStatsKL
 flag indicating the calculation of KL divergence between prior and posterior
 
bool posteriorStatsMutual
 flag indicating the calculation of mutual information between prior and posterior
 
bool posteriorStatsKDE
 flag indicating the calculation of the kernel density estimate of the posteriors
 
bool chainDiagnostics
 flag indicating calculation of chain diagnostics
 
bool chainDiagnosticsCI
 flag indicating calculation of confidence intervals as a chain diagnositc
 
bool calModelEvidence
 flag indicating calculation of the evidence of the model
 
bool calModelEvidMC
 flag indicating use of Monte Carlo approximation to calculate evidence
 
bool calModelEvidLaplace
 flag indicating use of Laplace approximation to calculate evidence
 
int evidenceSamples
 number of samples to be used in model evidence calculation
 
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
 
RealMatrix acceptanceChain
 Post-processing-related controls. More...
 
RealMatrix acceptedFnVals
 cached function values corresponding to acceptanceChain for final statistics reporting
 
std::map< Real, RealVector > bestSamples
 container for managing best MCMC samples (points and associated log posterior) collected across multiple (restarted) chains
 
int burnInSamples
 number of MCMC samples to discard from acceptance chain
 
int subSamplingPeriod
 period or skip in post-processing the acceptance chain
 
RealMatrix chainStats
 
RealMatrix fnStats
 
RealMatrix predVals
 Compute credibility and prediction intervals of final chain.
 
RealMatrix filteredFnVals
 cached filtered function values for printing (may be a view of acceptedFnVals)
 
String exportMCMCFilename
 output filename for the MCMC chain
 
short exportMCMCFormat
 output formatting options for MCMC export
 
short filteredMCMCFormat
 
Real kl_est
 
bool scaleFlag
 whether response scaling is active
 
bool weightFlag
 whether weight scaling is active
 
- Protected Attributes inherited from NonDCalibration
bool calibrationData
 flag indicating whether there is calibration data present
 
ExperimentData expData
 Container for experimental data to which to calibrate model.
 
- Protected Attributes inherited from NonD
NonDprevNondInstance
 pointer containing previous value of nondInstance
 
size_t startCAUV
 starting index of continuous aleatory uncertain variables within active continuous variables (convenience for managing offsets)
 
size_t numCAUV
 number of active continuous aleatory uncertain variables
 
bool epistemicStats
 flag for computing interval-type metrics instead of integrated metrics If any epistemic vars are active in a metric evaluation, then flag is set.
 
RealMatrix momentStats
 standardized or central resp moments, as determined by finalMomentsType. Calculated in compute_moments()) and indexed as (moment,fn).
 
RealVectorArray requestedRespLevels
 requested response levels for all response functions
 
RealVectorArray computedProbLevels
 output probability levels for all response functions resulting from requestedRespLevels
 
RealVectorArray computedRelLevels
 output reliability levels for all response functions resulting from requestedRespLevels
 
RealVectorArray computedGenRelLevels
 output generalized reliability levels for all response functions resulting from requestedRespLevels
 
short respLevelTarget
 indicates mapping of z->p (PROBABILITIES), z->beta (RELIABILITIES), or z->beta* (GEN_RELIABILITIES)
 
short respLevelTargetReduce
 indicates component or system series/parallel failure metrics
 
RealVectorArray requestedProbLevels
 requested probability levels for all response functions
 
RealVectorArray requestedRelLevels
 requested reliability levels for all response functions
 
RealVectorArray requestedGenRelLevels
 requested generalized reliability levels for all response functions
 
RealVectorArray computedRespLevels
 output response levels for all response functions resulting from requestedProbLevels, requestedRelLevels, or requestedGenRelLevels
 
size_t totalLevelRequests
 total number of levels specified within requestedRespLevels, requestedProbLevels, and requestedRelLevels
 
bool cdfFlag
 flag for type of probabilities/reliabilities used in mappings: cumulative/CDF (true) or complementary/CCDF (false)
 
bool pdfOutput
 flag for managing output of response probability density functions (PDFs)
 
RealVectorArray computedPDFAbscissas
 sorted response PDF intervals bounds extracted from min/max sample and requested/computedRespLevels (vector lengths = num bins + 1)
 
RealVectorArray computedPDFOrdinates
 response PDF densities computed from bin counts divided by (unequal) bin widths (vector lengths = num bins)
 
Response finalStatistics
 final statistics from the uncertainty propagation used in strategies: response means, standard deviations, and probabilities of failure
 
short finalMomentsType
 type of moments logged within finalStatistics: none, central, standard
 
size_t miPLIndex
 index for the active ParallelLevel within ParallelConfiguration::miPLIters
 
BitArray pdfComputed
 Whether PDF was computed for function i; used to determine whether a pdf should be archived.
 
- Protected Attributes inherited from Analyzer
size_t numFunctions
 number of response functions
 
size_t numContinuousVars
 number of active continuous vars
 
size_t numDiscreteIntVars
 number of active discrete integer vars
 
size_t numDiscreteStringVars
 number of active discrete string vars
 
size_t numDiscreteRealVars
 number of active discrete real vars
 
bool compactMode
 switch for allSamples (compact mode) instead of allVariables (normal mode)
 
VariablesArray allVariables
 array of all variables to be evaluated in evaluate_parameter_sets()
 
RealMatrix allSamples
 compact alternative to allVariables
 
IntResponseMap allResponses
 array of all responses to be computed in evaluate_parameter_sets()
 
StringArray allHeaders
 array of headers to insert into output while evaluating allVariables
 
size_t numObjFns
 number of objective functions
 
size_t numLSqTerms
 number of least squares terms
 
RealPairPRPMultiMap bestVarsRespMap
 map which stores best set of solutions
 
- Protected Attributes inherited from Iterator
ProblemDescDBprobDescDB
 class member reference to the problem description database More...
 
ParallelLibraryparallelLib
 class member reference to the parallel library
 
ParConfigLIter methodPCIter
 the active ParallelConfiguration used by this Iterator instance
 
Model iteratedModel
 the model to be iterated (for iterators and meta-iterators employing a single model instance)
 
size_t myModelLayers
 number of Models locally (in Iterator or derived classes) wrapped around the initially passed in Model
 
unsigned short methodName
 name of the iterator (the user's method spec)
 
Real convergenceTol
 iteration convergence tolerance
 
size_t maxIterations
 maximum number of iterations for the method
 
size_t maxFunctionEvals
 maximum number of fn evaluations for the method
 
int maxEvalConcurrency
 maximum number of concurrent model evaluations More...
 
ActiveSet activeSet
 the response data requirements on each function evaluation
 
size_t numFinalSolutions
 number of solutions to retain in best variables/response arrays
 
VariablesArray bestVariablesArray
 collection of N best solution variables found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)
 
ResponseArray bestResponseArray
 collection of N best solution responses found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)
 
bool subIteratorFlag
 flag indicating if this Iterator is a sub-iterator (NestedModel::subIterator or DataFitSurrModel::daceIterator)
 
short outputLevel
 output verbosity level: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT
 
bool summaryOutputFlag
 flag for summary output (evaluation stats, final results); default true, but false for on-the-fly (helper) iterators and sub-iterator use cases
 
ResultsManagerresultsDB
 reference to the global iterator results database
 
EvaluationStore & evaluationsDB
 reference to the global evaluation database
 
EvaluationsDBState evaluationsDBState
 State of evaluations DB for this iterator.
 
ResultsNames resultsNames
 valid names for iterator results
 
std::shared_ptr< TraitsBasemethodTraits
 pointer that retains shared ownership of a TraitsBase object, or child thereof
 
bool topLevel
 Whether this is the top level iterator.
 
bool exportSurrogate = false
 whether to export final surrogates
 
String surrExportPrefix
 base filename for exported surrogates
 
unsigned short surrExportFormat = NO_MODEL_FORMAT
 (bitwise) format(s) to export
 

Private Attributes

Iterator lhsIter
 LHS iterator for generating samples for GP.
 

Static Private Attributes

static NonDGPMSABayesCalibrationnonDGPMSAInstance
 Pointer to current class instance for use in static callback functions.
 

Additional Inherited Members

- Static Public Member Functions inherited from NonDBayesCalibration
static void get_positive_definite_covariance_from_hessian (const RealSymMatrix &hessian, const RealMatrix &prior_chol_fact, RealSymMatrix &covariance, short output_lev)
 Compute the proposal covariance C based on low-rank approximation to the prior-preconditioned misfit Hessian.
 
static Real knn_kl_div (RealMatrix &distX_samples, RealMatrix &distY_samples, size_t dim)
 
static Real knn_mutual_info (RealMatrix &Xmatrix, int dimX, int dimY, unsigned short alg)
 
- Static Protected Member Functions inherited from NonDQUESOBayesCalibration
static double dakotaLogLikelihood (const QUESO::GslVector &paramValues, const QUESO::GslVector *paramDirection, const void *functionDataPtr, QUESO::GslVector *gradVector, QUESO::GslMatrix *hessianMatrix, QUESO::GslVector *hessianEffect)
 Log Likelihood function for call-back from QUESO to DAKOTA for evaluation.
 
- Static Protected Member Functions inherited from NonDBayesCalibration
static void neg_log_post_resp_mapping (const Variables &model_vars, const Variables &nlpost_vars, const Response &model_resp, Response &nlpost_resp)
 static function passed by pointer to negLogPostModel recast model More...
 
static void ann_dist (const ANNpointArray matrix1, const ANNpointArray matrix2, RealVector &distances, int NX, int NY, int dim2, IntVector &k, double eps)
 
static void ann_dist (const ANNpointArray matrix1, const ANNpointArray matrix2, RealVector &distances, Int2DArray &indices, int NX, int NY, int dim2, IntVector &k, double eps)
 
- Static Protected Member Functions inherited from Iterator
static void gnewton_set_recast (const Variables &recast_vars, const ActiveSet &recast_set, ActiveSet &sub_model_set)
 conversion of request vector values for the Gauss-Newton Hessian approximation More...
 
- Static Protected Attributes inherited from NonDQUESOBayesCalibration
static NonDQUESOBayesCalibrationnonDQUESOInstance
 Pointer to current class instance for use in static callback functions.
 
- Static Protected Attributes inherited from NonDBayesCalibration
static NonDBayesCalibrationnonDBayesInstance
 Pointer to current class instance for use in static callback functions.
 
- Static Protected Attributes inherited from NonD
static NonDnondInstance
 pointer to the active object instance used within static evaluator functions in order to avoid the need for static data
 

Detailed Description

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.

Constructor & Destructor Documentation

◆ NonDGPMSABayesCalibration()

NonDGPMSABayesCalibration ( ProblemDescDB problem_db,
Model model 
)

Member Function Documentation

◆ calibrate()

void calibrate ( )
protectedvirtual

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 NonDBayesCalibration.

References NonDQUESOBayesCalibration::advancedOptionsFile, ExperimentData::all_data(), NonDGPMSABayesCalibration::buildSamples, NonDGPMSABayesCalibration::cache_acceptance_chain(), NonDQUESOBayesCalibration::calIpMhOptionsValues, NonDGPMSABayesCalibration::configSpace, NonDCalibration::expData, NonDGPMSABayesCalibration::experimentSpace, NonDGPMSABayesCalibration::fill_experiment_data(), NonDGPMSABayesCalibration::fill_simulation_data(), NonDGPMSABayesCalibration::gpmsaConfigVars, NonDGPMSABayesCalibration::gpmsaFactory, NonDGPMSABayesCalibration::gpmsaNormalize, NonDGPMSABayesCalibration::gpmsaOptions, NonDGPMSABayesCalibration::init_queso_solver(), NonDQUESOBayesCalibration::inverseProb, NonDQUESOBayesCalibration::mcmcType, NonDGPMSABayesCalibration::nEtaSpace, NonDGPMSABayesCalibration::nonDGPMSAInstance, NonDQUESOBayesCalibration::nonDQUESOInstance, ExperimentData::num_experiments(), Analyzer::numContinuousVars, Analyzer::numFunctions, NonDBayesCalibration::numHyperparams, Iterator::outputLevel, NonDGPMSABayesCalibration::overlay_initial_params(), NonDGPMSABayesCalibration::overlay_proposal_covariance(), NonDQUESOBayesCalibration::paramSpace, NonDQUESOBayesCalibration::priorRv, NonDQUESOBayesCalibration::quesoEnv, and NonDBayesCalibration::standardizedSpace.

◆ fill_simulation_data()

void fill_simulation_data ( )
protected

populate the simulation data, calculating and applying scaling if needed

simulation data, one row per simulation build sample, columns for calibration variables, configuration variables, function values (duplicates storage, but unifies import vs. DOE cases)

References NonDGPMSABayesCalibration::acquire_simulation_data(), NonDGPMSABayesCalibration::buildSamples, NonDGPMSABayesCalibration::configSpace, NonDGPMSABayesCalibration::gpmsaConfigVars, NonDGPMSABayesCalibration::gpmsaFactory, NonDGPMSABayesCalibration::nEtaSpace, Analyzer::numContinuousVars, Analyzer::numFunctions, NonDQUESOBayesCalibration::paramSpace, and NonDGPMSABayesCalibration::userConfigVars.

Referenced by NonDGPMSABayesCalibration::calibrate().

◆ cache_acceptance_chain()

void cache_acceptance_chain ( )
protected

◆ print_results()

void print_results ( std::ostream &  s,
short  results_state = FINAL_RESULTS 
)
protectedvirtual

print the final iterator results

This virtual function provides additional iterator-specific final results outputs beyond the function evaluation summary printed in finalize_run().

Reimplemented from NonDBayesCalibration.

References NonDBayesCalibration::print_results().


The documentation for this class was generated from the following files: