Base class for Bayesian inference: generates posterior distribution on model parameters given experimental data. More...

Inheritance diagram for NonDBayesCalibration:

Public Member Functions
	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)

Model &	iterated_model ()
	return the iteratedModel (iterators & meta-iterators using a single model instance)

ProblemDescDB &	problem_description_db () const
	return the problem description database (probDescDB)

ParallelLibrary &	parallel_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 ActiveSet &	active_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< Iterator >	iterator_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< TraitsBase >	traits () 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.

Static Public Member Functions
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)

Protected Member Functions
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	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

virtual void	print_results (std::ostream &s, short results_state=FINAL_RESULTS)
	print the final iterator results More...

void	print_variables (std::ostream &s, const RealVector &c_vars)
	convenience function to print calibration parameters, e.g., for MAP / best parameters

const Model &	algorithm_space_model () const

virtual void	specify_prior ()
	Methods for instantiating a Bayesian inverse problem. No-ops in the base class that can be specialized by child classes.

virtual void	specify_likelihood ()

virtual void	init_bayesian_solver ()

virtual void	specify_posterior ()

virtual void	calibrate ()=0
	Perform Bayesian calibration (all derived classes must implement)

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

virtual void	map_pre_solve ()

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...

virtual void	filter_chain_by_conditioning ()
	Filter mcmc chain for PCE adaptive emulator. 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	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 Response &	response_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 Variables &	variables_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...

Static Protected Member Functions
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...

Protected Attributes
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
NonD *	prevNondInstance
	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
ProblemDescDB &	probDescDB
	class member reference to the problem description database More...

ParallelLibrary &	parallelLib
	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

ResultsManager &	resultsDB
	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< TraitsBase >	methodTraits
	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

Static Protected Attributes
static NonDBayesCalibration *	nonDBayesInstance
	Pointer to current class instance for use in static callback functions.

Static Protected Attributes inherited from NonD
static NonD *	nondInstance
	pointer to the active object instance used within static evaluator functions in order to avoid the need for static data

Detailed Description

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.

Constructor & Destructor Documentation

◆ NonDBayesCalibration()

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.

Member Function Documentation

◆ prior_mean()

void prior_mean ( VectorType & mean_vec ) const

return the mean of the prior distribution

Assume the target mean_vec is sized by client

References NonDBayesCalibration::invGammaDists, Iterator::iteratedModel, Model::multivariate_distribution(), Analyzer::numContinuousVars, NonDBayesCalibration::numHyperparams, NonDBayesCalibration::residualModel, and NonDBayesCalibration::standardizedSpace.

◆ prior_variance()

void prior_variance ( MatrixType & var_mat ) const

return the covariance of the prior distribution

Assumes the target var_mat is sized by client

References NonDBayesCalibration::invGammaDists, Iterator::iteratedModel, Model::multivariate_distribution(), Analyzer::numContinuousVars, NonDBayesCalibration::numHyperparams, NonDBayesCalibration::residualModel, and NonDBayesCalibration::standardizedSpace.

◆ pre_run()

void pre_run ( )

protectedvirtual

pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori

pre-run phase, which a derived iterator may optionally reimplement; when not present, pre-run is likely integrated into the derived run function. This is a virtual function; when re-implementing, a derived class must call its nearest parent's pre_run(), if implemented, typically before performing its own implementation steps.

Reimplemented from Analyzer.

References NonDBayesCalibration::construct_map_optimizer(), Model::is_null(), NonDBayesCalibration::negLogPostModel, Analyzer::pre_run(), NonDBayesCalibration::residualModel, and Model::update_from_subordinate_model().

◆ core_run()

void core_run ( )

protectedvirtual

core portion of run; implemented by all derived classes and may include pre/post steps in lieu of separate pre/post

Virtual run function for the iterator class hierarchy. All derived classes need to redefine it.

Reimplemented from Iterator.

References NonDBayesCalibration::adaptExpDesign, NonDBayesCalibration::adaptPosteriorRefine, NonDBayesCalibration::build_model_discrepancy(), NonDBayesCalibration::calibrate(), NonDBayesCalibration::calibrate_to_hifi(), NonDBayesCalibration::calibrate_with_adaptive_emulator(), NonDBayesCalibration::calModelDiscrepancy, NonDBayesCalibration::compute_statistics(), NonDBayesCalibration::initialize_model(), NonDBayesCalibration::nonDBayesInstance, and NonDBayesCalibration::specify_prior().

◆ 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 Analyzer.

Reimplemented in NonDWASABIBayesCalibration, NonDQUESOBayesCalibration, NonDMUQBayesCalibration, and NonDGPMSABayesCalibration.

References NonDBayesCalibration::chainDiagnostics, Model::continuous_variable_labels(), Dakota::copy_data(), Model::current_response(), NonDBayesCalibration::filteredFnVals, Response::function_labels(), Dakota::length(), NonDBayesCalibration::mcmcModel, Iterator::outputLevel, NonDBayesCalibration::posteriorStatsKL, NonDBayesCalibration::predVals, NonDSampling::print_moments(), NonD::requestedProbLevels, and NonDBayesCalibration::residualModel.

Referenced by NonDGPMSABayesCalibration::print_results(), NonDMUQBayesCalibration::print_results(), and NonDQUESOBayesCalibration::print_results().

◆ algorithm_space_model()

const Model & algorithm_space_model ( ) const

inlineprotectedvirtual

default definition that gets redefined in selected derived Minimizers

Reimplemented from Analyzer.

References NonDBayesCalibration::residualModel.

◆ init_map_optimizer()

void init_map_optimizer ( )

protected

initialize the MAP optimizer selection

Construct optimizer for MAP pre-solve Emulator: on by default; can be overridden with "pre_solve none" No emulator: off by default; can be activated with "pre_solve {sqp,nip}" relies on mapOptimizer ctor to enforce min derivative support Calculation of model evidence using Laplace approximation: this requires a MAP solve.

References Dakota::abort_handler(), NonDBayesCalibration::calModelEvidLaplace, NonDBayesCalibration::emulatorType, and NonDBayesCalibration::mapOptAlgOverride.

Referenced by NonDBayesCalibration::NonDBayesCalibration().

◆ map_pre_solve()

void map_pre_solve ( )

protectedvirtual

Runs a pre-solve for the MAP point. If running calibrate_to_hifi() or calibrate_with_adaptive_emulator(), propagates the solution to the mapSoln variable. Returns the optimal solution as a vector.

References Variables::continuous_variables(), Dakota::copy_data(), Model::current_variables(), Iterator::is_null(), NonDBayesCalibration::mapOptimizer, NonDBayesCalibration::mapSoln, NonDBayesCalibration::negLogPostModel, NonDBayesCalibration::print_variables(), Iterator::run(), and Iterator::variables_results().

◆ calibrate_with_adaptive_emulator()

void calibrate_with_adaptive_emulator ( )

protected

Run calibration, looping to refine emulator around posterior mode.

This method will perform a Bayesian calibration with an emulator, but periodically the emulator is updated with more sample points from the original model in the high-posterior-density region of parameter space.

References Dakota::abort_handler(), NonDBayesCalibration::assess_emulator_convergence(), NonDBayesCalibration::best_to_all(), NonDBayesCalibration::calibrate(), Analyzer::compactMode, Iterator::convergenceTol, NonDBayesCalibration::emulatorType, NonDBayesCalibration::filter_chain_by_conditioning(), Iterator::maxIterations, and NonDBayesCalibration::update_model().

Referenced by NonDBayesCalibration::core_run().

◆ build_designs()

void build_designs ( VariablesArray & design_matrix )

protected

build matrix of candidate points

On entry, design_matrix already sized to numCandidates.

References Iterator::all_variables(), Iterator::assign_rep(), NonDBayesCalibration::hifiModel, NonDBayesCalibration::importCandFormat, NonDBayesCalibration::importCandPtsFile, NonDBayesCalibration::numCandidates, Iterator::outputLevel, Iterator::pre_run(), NonDBayesCalibration::randomSeed, and Analyzer::vary_pattern().

Referenced by NonDBayesCalibration::calibrate_to_hifi().

◆ log_likelihood()

Real log_likelihood	(	const RealVector &	residuals,
		const RealVector &	all_params
	)

protected

calculate log-likelihood from the passed residuals (assuming they are already sized and scaled by covariance / hyperparams...

Calculate the log-likelihood, accounting for contributions from covariance and hyperparameters, as well as constant term:

log(L) = -1/2*Nr*log(2*pi) - 1/2*log(det(Cov)) - 1/2*r'(Cov^{-1})*r

The passed residuals must already be size-adjusted, differenced with any data, if present, and scaled by covariance^{-1/2}.

References NonDCalibration::expData, Dakota::HALF_LOG_2PI, ExperimentData::half_log_cov_determinant(), Analyzer::numContinuousVars, NonDBayesCalibration::numHyperparams, and NonDBayesCalibration::obsErrorMultiplierMode.

Referenced by NonDBayesCalibration::calculate_evidence(), NonDQUESOBayesCalibration::dakotaLogLikelihood(), NonDBayesCalibration::neg_log_post_resp_mapping(), and NonDDREAMBayesCalibration::sample_likelihood().

◆ neg_log_post_resp_mapping()

void neg_log_post_resp_mapping	(	const Variables &	residual_vars,
		const Variables &	nlpost_vars,
		const Response &	residual_resp,
		Response &	nlpost_resp
	)

staticprotected

static function passed by pointer to negLogPostModel recast model

Response mapping callback used by RecastModel for MAP pre-solve. Computes

-log(post) = -log(like) - log(prior); where -log(like) = 1/2*Nr*log(2*pi) + 1/2*log(det(Cov)) + 1/2*r'(Cov^{-1})*r = 1/2*Nr*log(2*pi) + 1/2*log(det(Cov)) + misfit

(misfit defined as 1/2 r^T (mult^2*Gamma_d)^{-1} r) The passed residual_resp has been differenced, interpolated, and covariance-scaled

References Response::active_set_request_vector(), NonDBayesCalibration::augment_gradient_with_log_prior(), NonDBayesCalibration::augment_hessian_with_log_prior(), ExperimentData::build_gradient_of_sum_square_residuals(), ExperimentData::build_hessian_of_sum_square_residuals(), Variables::continuous_variables(), NonDCalibration::expData, Response::function_gradient_view(), Response::function_hessian_view(), Response::function_value(), Response::function_values(), ExperimentData::half_log_cov_det_gradient(), ExperimentData::half_log_cov_det_hessian(), NonDBayesCalibration::log_likelihood(), NonDBayesCalibration::log_prior_density(), NonDBayesCalibration::nonDBayesInstance, Analyzer::numContinuousVars, NonDBayesCalibration::numHyperparams, NonDBayesCalibration::obsErrorMultiplierMode, and Iterator::outputLevel.

Referenced by NonDBayesCalibration::calculate_evidence(), and NonDBayesCalibration::construct_map_model().

◆ scale_model()

void scale_model ( )

protected

Wrap iteratedModel in a RecastModel that performs response scaling.

Wrap the residualModel in a scaling transformation, such that residualModel now contains a scaling recast model.

References Model::assign_rep(), Iterator::outputLevel, and NonDBayesCalibration::residualModel.

Referenced by NonDBayesCalibration::NonDBayesCalibration().

◆ weight_model()

void weight_model ( )

protected

Wrap iteratedModel in a RecastModel that weights the residuals.

Setup Recast for weighting model. The weighting transformation doesn't resize, and makes no vars, active set or secondary mapping. All indices are one-to-one mapped (no change in counts).

References Dakota::abort_handler(), Model::assign_rep(), Iterator::outputLevel, Model::primary_response_fn_weights(), and NonDBayesCalibration::residualModel.

Referenced by NonDBayesCalibration::NonDBayesCalibration().

◆ export_chain()

void export_chain	(	RealMatrix &	filtered_chain,
		RealMatrix &	filtered_fn_vals
	)

protected

Print filtered posterior and function values (later: credibility and prediction intervals)

Print tabular file with filtered chain, function values, and pred values

References Variables::continuous_variables(), Variables::copy(), Model::current_response(), Model::current_variables(), NonDBayesCalibration::exportMCMCFilename, NonDBayesCalibration::exportMCMCFormat, Response::function_labels(), Model::interface_id(), NonDBayesCalibration::mcmcModel, Analyzer::numFunctions, NonDBayesCalibration::residualModel, Dakota::write_precision, and Variables::write_tabular().

Member Data Documentation

◆ acceptanceChain

RealMatrix acceptanceChain

protected

Post-processing-related controls.

accumulation of acceptance chain across restarts (stored in user-space) TO DO: retire once restarts are retired; optimize to convert to user-space only in final results

Referenced by NonDDREAMBayesCalibration::archive_acceptance_chain(), NonDGPMSABayesCalibration::cache_acceptance_chain(), NonDMUQBayesCalibration::cache_chain(), NonDQUESOBayesCalibration::cache_chain(), NonDDREAMBayesCalibration::cache_chain(), NonDBayesCalibration::calculate_kde(), NonDBayesCalibration::calibrate_to_hifi(), NonDBayesCalibration::compute_statistics(), and NonDBayesCalibration::kl_post_prior().

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

NonDBayesCalibration.hpp
NonDBayesCalibration.cpp

Public Member Functions

Static Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Static Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ NonDBayesCalibration()

Member Function Documentation

◆ prior_mean()

◆ prior_variance()

◆ pre_run()

◆ core_run()

◆ print_results()

◆ algorithm_space_model()

◆ init_map_optimizer()

◆ map_pre_solve()

◆ calibrate_with_adaptive_emulator()

◆ build_designs()

◆ log_likelihood()

◆ neg_log_post_resp_mapping()

◆ scale_model()

◆ weight_model()

◆ export_chain()

Member Data Documentation

◆ acceptanceChain