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NonDMUQBayesCalibration Class Reference

Dakota interface to MUQ (MIT Uncertainty Quantification) library. More...

Inheritance diagram for NonDMUQBayesCalibration:
NonDBayesCalibration NonDCalibration NonD Analyzer Iterator

Public Member Functions

 NonDMUQBayesCalibration (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 ~NonDMUQBayesCalibration ()
 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
 
int 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()))
 alternate envelope constructor that assigns a representation pointer 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 RealVector & 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 unsigned short uses_method () const
 return name of any enabling iterator used by this iterator More...
 
virtual void method_recourse ()
 perform a method switch, if possible, due to a detected conflict
 
virtual void sampling_reset (int min_samples, bool all_data_flag, bool stats_flag)
 reset sampling iterator to use at least min_samples
 
virtual void sampling_reference (int 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 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 (const bool &tflag)
 Set the iterator's top level flag.
 

Protected Member Functions

void calibrate ()
 
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
 
void cache_chain ()
 cache the chain to acceptanceChain and acceptedFnVals More...
 
void log_best ()
 log at most batchSize best chain points into bestSamples
 
void specify_prior () override
 initialize the MUQ parameter space, min, max, initial, domain, and prior define solver options, likelihood callback, posterior RV, and inverse problem
 
void specify_likelihood () override
 
void init_bayesian_solver () override
 
void specify_posterior () override
 
void init_proposal_covariance ()
 set the proposal covariance matrix
 
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 ()
 
- 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 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 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
 
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 Attributes

std::shared_ptr
< muq::Modeling::WorkGraph > 
workGraph
 
std::shared_ptr
< muq::Modeling::IdentityOperator > 
parameterPtr
 
std::shared_ptr
< muq::Modeling::Distribution > 
distPtr
 
std::shared_ptr
< muq::Modeling::DensityProduct > 
posteriorPtr
 
std::shared_ptr< MUQLikelihood > MUQLikelihoodPtr
 
std::shared_ptr< MUQPrior > MUQPriorPtr
 
std::shared_ptr
< muq::SamplingAlgorithms::SingleChainMCMC > 
mcmc
 
std::shared_ptr
< muq::SamplingAlgorithms::SampleCollection > 
samps
 
String mcmcType
 MCMC type ("dram" or "delayed_rejection" or "adaptive_metropolis" or "metropolis_hastings" or "multilevel", within QUESO)
 
unsigned int numBestSamples
 number of best samples (max log_posterior values) to keep
 
Eigen::MatrixXd proposalCovMatrix
 proposal covariance for MCMC
 
double priorPropCovMult
 optional multiplier to scale prior-based proposal covariance
 
RealVector init_point
 initial guess (user-specified or default initial values)
 
- 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
 

Static Protected Attributes

static NonDMUQBayesCalibrationnonDMUQInstance
 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.
 

Friends

class MUQLikelihood
 
class MUQPrior
 

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 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)
 

Detailed Description

Dakota interface to MUQ (MIT Uncertainty Quantification) library.

This class performs Bayesian calibration using the MUQ library

Constructor & Destructor Documentation

NonDMUQBayesCalibration ( ProblemDescDB problem_db,
Model model 
)

standard constructor

This constructor is called for a standard letter-envelope iterator instantiation. In this case, set_db_list_nodes has been called and probDescDB can be queried for settings from the method specification.

References NonDBayesCalibration::proposalCovarType.

Member Function Documentation

void calibrate ( )
protectedvirtual
void print_results ( std::ostream &  s,
short  results_state = FINAL_RESULTS 
)
protectedvirtual
void cache_chain ( )
protected
void prior_proposal_covariance ( )
protected
void user_proposal_covariance ( const String &  input_fmt,
const RealVector &  cov_data,
const String &  cov_filename 
)
protected

set proposal covariance from user-provided diagonal or matrix

This function will convert user-specified cov_type = "diagonal" | "matrix" data from either cov_data or cov_filename and populate a full Eigen::MatrixXd in proposalCovMatrix with the covariance.

References Dakota::length(), Analyzer::numContinuousVars, NonDMUQBayesCalibration::proposalCovMatrix, Dakota::read_unsized_data(), and NonDBayesCalibration::standardizedSpace.

Referenced by NonDMUQBayesCalibration::init_proposal_covariance().


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