Dakota  Version 6.4
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Pages
Public Member Functions | Protected Member Functions | Static Protected Member Functions | Protected Attributes | Private Attributes | Static Private Attributes | List of all members
NonDQUESOBayesCalibration Class Reference

Bayesian inference using the QUESO library from UT Austin. More...

Inheritance diagram for NonDQUESOBayesCalibration:
NonDBayesCalibration NonDCalibration NonD Analyzer Iterator

Public Member Functions

 NonDQUESOBayesCalibration (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 ~NonDQUESOBayesCalibration ()
 destructor
 
- Public Member Functions inherited from NonDBayesCalibration
 NonDBayesCalibration (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 ~NonDBayesCalibration ()
 destructor
 
template<typename VectorType >
Real prior_density (const VectorType &vec)
 compute the prior PDF for a particular MCMC sample
 
template<typename VectorType >
Real log_prior_density (const VectorType &vec)
 compute the log prior PDF for a particular MCMC sample
 
template<typename Engine >
void prior_sample (Engine &rng, RealVector &prior_samples)
 draw a multivariate sample from the prior distribution
 
- 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 initialize_random_variables (short u_space_type)
 initialize natafTransform based on distribution data from iteratedModel More...
 
void initialize_random_variables (const Pecos::ProbabilityTransformation &transform, bool deep_copy=false)
 alternate form: initialize natafTransform based on incoming data More...
 
void initialize_random_variable_transformation ()
 instantiate natafTransform
 
void initialize_random_variable_types ()
 initializes ranVarTypesX within natafTransform (u-space not needed) More...
 
void initialize_random_variable_types (short u_space_type)
 initializes ranVarTypesX and ranVarTypesU within natafTransform More...
 
void initialize_random_variable_parameters ()
 initializes ranVarMeansX, ranVarStdDevsX, ranVarLowerBndsX, ranVarUpperBndsX, and ranVarAddtlParamsX within natafTransform More...
 
void initialize_random_variable_correlations ()
 propagate iteratedModel correlations to natafTransform
 
void verify_correlation_support (short u_space_type)
 verify that correlation warping is supported by Nataf for given variable types
 
void transform_correlations ()
 perform correlation warping for variable types supported by Nataf
 
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 distribution_parameter_derivatives (bool dist_param_derivs)
 set distParamDerivs
 
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...
 
bool pdf_output () const
 get pdfOutput
 
void pdf_output (bool output)
 set pdfOutput
 
Pecos::ProbabilityTransformation & variable_transformation ()
 return natafTransform
 
- 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 ()
 default constructor More...
 
 Iterator (ProblemDescDB &problem_db)
 standard envelope constructor, which constructs its own model(s) More...
 
 Iterator (ProblemDescDB &problem_db, Model &model)
 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)
 alternate envelope constructor for instantiations by name without the ProblemDescDB More...
 
 Iterator (const Iterator &iterator)
 copy constructor More...
 
virtual ~Iterator ()
 destructor More...
 
Iterator operator= (const Iterator &iterator)
 assignment operator More...
 
virtual void post_input ()
 read tabular data for post-run mode
 
virtual void reset ()
 restore initial state for repeated sub-iterator executions
 
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)
 used by IteratorScheduler to unpack starting data for an iterator run
 
virtual void unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer)
 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 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_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 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 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
 
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 (Iterator *iterator_rep, bool ref_count_incr=true)
 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_name) const
 convert a method name enumeration value to a string
 
unsigned short method_string_to_enum (const String &method_name) const
 convert a method name string to an enumeration value
 
String submethod_enum_to_string (unsigned short submethod_name) const
 convert a 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
 
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 vector (for use with iterators that employ evaluate_parameter_sets())
 
const ActiveSetactive_set () const
 return the default active set vector (used by iterators that employ evaluate_parameter_sets())
 
void sub_iterator_flag (bool si_flag)
 set subIteratorFlag (and update summaryOutputFlag if needed)
 
void active_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
 
bool is_null () const
 function to check iteratorRep (does this envelope contain a letter?)
 
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...
 

Protected Member Functions

void core_run ()
 
void print_results (std::ostream &s)
 print the final iterator results More...
 
void init_queso_environment ()
 initialize the QUESO FullEnvironment on the Dakota MPIComm
 
void init_precond_request_value ()
 initialize the ASV value for preconditioned cases
 
void init_queso_solver ()
 define variables, options, likelihood callback, and inverse problem
 
void precondition_proposal ()
 use derivative information from the emulator to define the proposal covariance (inverse of misfit Hessian)
 
void run_queso_solver ()
 perform the MCMC process
 
void run_chain_with_restarting ()
 short term option to restart the MCMC chain with updated proposal density computed from the emulator at a new starting point
 
void accumulate_chain (size_t update_cntr)
 accumulate unique samples drawn from the acceptance chain
 
void aggregate_acceptance_chain (size_t update_cntr)
 accumulate the acceptance chain across multiple restart cycles, including recovering corresponding function values More...
 
void filter_chain_by_probability (size_t update_cntr, unsigned short batch_size)
 extract batch_size points from the MCMC chain and store final aggregated set within allSamples; unique points with highest posterior probability are selected
 
void filter_chain_by_conditioning (size_t update_cntr, unsigned short batch_size)
 extract batch_size 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 chain_to_local (unsigned short batch_size, std::map< Real, size_t > &local_best)
 store indices of best batch_size samples from the current MCMC chain within the local_best array
 
void local_to_aggregated (unsigned short batch_size, const std::map< Real, size_t > &local_best)
 update bestSamples aggregation using new contributions from the current MCMC chain
 
void aggregated_to_all ()
 following aggregation cycles, copy bestSamples to allSamples
 
void local_to_all (const std::map< Real, size_t > &local_best)
 in the absence of aggregation cycles, copy local_best to allSamples
 
void update_center ()
 update the starting point for a restarted MCMC chain using last point from previous chain
 
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 init_parameter_domain ()
 intialize the QUESO parameter space, min, max, initial, and domain
 
void prior_proposal_covariance ()
 use covariance of prior distribution for setting proposal covariance More...
 
void user_proposal_covariance (const String &input_fmt, const RealVector &cov_data, const String &cov_filename)
 set proposal covariance from user-provided diagonal or matrix More...
 
void validate_proposal ()
 
void set_ip_options ()
 set inverse problem options calIpOptionsValues common to all solvers More...
 
void set_mh_options ()
 set MH-specific inverse problem options calIpMhOptionsValues
 
void update_chain_size (unsigned int size)
 update MH-specific inverse problem options calIpMhOptionsValues
 
void copy_gsl (const QUESO::GslVector &qv, RealVector &rv)
 local copy_data utility from GslVector to RealVector
 
void copy_gsl (const RealVector &rv, QUESO::GslVector &qv)
 local copy_data utility from RealVector to GslVector
 
void copy_gsl_partial (const QUESO::GslVector &qv, size_t start, RealVector &rv)
 local copy_data utility from portion of GslVector to RealVector
 
void copy_gsl_partial (const RealVector &rv, QUESO::GslVector &qv, size_t start)
 local copy_data utility from RealVector to portion of GslVector
 
void copy_gsl (const QUESO::GslVector &qv, RealMatrix &rm, int i)
 local copy_data utility from GslVector to column in RealMatrix
 
bool equal_gsl (const QUESO::GslVector &qv1, const QUESO::GslVector &qv2)
 equality tester for two GslVectors
 
- Protected Member Functions inherited from NonDBayesCalibration
void derived_init_communicators (ParLevLIter pl_iter)
 derived class contributions to initializing the communicators associated with this Iterator instance
 
void derived_set_communicators (ParLevLIter pl_iter)
 derived class contributions to setting the communicators associated with this Iterator instance
 
void derived_free_communicators (ParLevLIter pl_iter)
 derived class contributions to freeing the communicators associated with this Iterator instance
 
const Modelalgorithm_space_model () const
 
void initialize_model ()
 initialize emulator model and probability space transformations
 
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 compute_statistics ()
 Compute final stats for MCMC chains.
 
void export_chain ()
 export the acceptance chain in user space
 
void filter_chain (RealMatrix &acceptance_chain, RealMatrix &filtered_chain)
 Perform chain filtering with burn-in and sub-sampling.
 
void filter_fnvals (RealMatrix &accepted_fn_vals, RealMatrix &filtered_fn_vals)
 
void compute_intervals (RealMatrix &acceptance_chain, RealMatrix &accepted_fn_vals)
 
void compute_prediction_vals (RealMatrix &filtered_fn_vals, RealMatrix &PredVals, int num_filtered, size_t num_exp, size_t num_concatenated)
 
void compute_col_means (RealMatrix &matrix, RealVector &avg_vals)
 
void compute_col_stdevs (RealMatrix &matrix, RealVector &avg_vals, RealVector &std_devs)
 
void print_filtered_tabular (RealMatrix &filteredChain, RealMatrix &filteredFnVals_for_intervals, RealMatrix &PredVals, int num_filtered, size_t num_exp)
 Print credibility and prediction intervals.
 
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)
 
- Protected Member Functions inherited from NonDCalibration
void set_configuration_vars (Model &model, const RealVector &x)
 set the passed configuration variables into the model's state vars
 
- 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, const RealVector &lower_bnds, const RealVector &upper_bnds)
 alternate constructor for sample generation "on the fly"
 
 ~NonD ()
 destructor
 
void initialize_run ()
 utility function to perform common operations prior to pre_run(); typically memory initialization; setting of instance pointers More...
 
void finalize_run ()
 utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More...
 
const Responseresponse_results () const
 return the final statistics from the nondeterministic iteration
 
void response_results_active_set (const ActiveSet &set)
 set the active set within finalStatistics
 
virtual void initialize_response_covariance ()
 initializes respCovariance
 
virtual void initialize_final_statistics ()
 initializes finalStatistics for storing NonD final results More...
 
virtual void update_final_statistics ()
 update finalStatistics::functionValues
 
void size ()
 Size local variables.
 
int generate_system_seed ()
 create a system-generated unique seed (when a seed is unspecified)
 
void initialize_final_statistics_gradients ()
 initializes finalStatistics::functionGradients
 
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 transform_model (Model &x_model, Model &u_model, bool truncated_bounds=false, Real bound=10.)
 recast x_model from x-space to u-space to create u_model
 
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
 
void archive_allocate_mappings ()
 allocate results array storage for distribution mappings
 
void archive_from_resp (size_t fn_index)
 archive the mappings from specified response levels for specified fn
 
void archive_to_resp (size_t fn_index)
 archive the mappings to computed response levels for specified fn
 
void archive_allocate_pdf ()
 allocate results array storage for pdf histograms
 
void archive_pdf (size_t fn_index)
 archive a single pdf histogram for specified function
 
- 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)
 alternate constructor for instantiations "on the fly" without a Model
 
 ~Analyzer ()
 destructor
 
virtual void get_parameter_sets (Model &model)
 Returns one block of samples (ndim * num_samples)
 
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 pre_run ()
 pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...
 
void post_run (std::ostream &s)
 post-run portion of run (optional); verbose to print results; re-implemented by Iterators that can read all Variables/Responses and perform final analysis phase in a standalone way More...
 
void pre_output ()
 
const Variablesvariables_results () const
 return a single final iterator solution (variables)
 
const VariablesArray & variables_array_results ()
 return multiple final iterator solutions (variables). This should only be used if returns_multiple_points() returns true.
 
const ResponseArray & response_array_results ()
 return multiple final iterator solutions (response). This should only be used if returns_multiple_points() returns true.
 
bool compact_mode () const
 returns Analyzer::compactMode
 
bool returns_multiple_points () const
 indicates if this iterator returns multiple final points. Default return is false. Override to return true if appropriate.
 
void evaluate_parameter_sets (Model &model, bool log_resp_flag, bool log_best_flag)
 perform function evaluations to map parameter sets (allVariables) into response sets (allResponses) More...
 
void variance_based_decomp (int ncont, int ndiscint, int ndiscreal, int num_samples)
 
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)
 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)
 alternate constructor for base iterator classes constructed on the fly More...
 
 Iterator (NoDBBaseConstructor, unsigned short method_name)
 alternate constructor for base iterator classes constructed on the fly 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
 

Static Protected Member Functions

static double dakotaLogLikelihood (const QUESO::GslVector &paramValues, const QUESO::GslVector *paramDirection, const void *functionDataPtr, QUESO::GslVector *gradVector, QUESO::GslMatrix *hessianMatrix, QUESO::GslVector *hessianEffect)
 Log Likelihood function for call-back from QUESO to DAKOTA for evaluation.
 
- Static Protected Member Functions inherited from NonDBayesCalibration
static void neg_log_post_resp_mapping (const Variables &model_vars, const Variables &nlpost_vars, const Response &model_resp, Response &nlpost_resp)
 static function passed by pointer to negLogPostModel recast model More...
 

Protected Attributes

String mcmcType
 MCMC type ("dram" or "delayed_rejection" or "adaptive_metropolis" or "metropolis_hastings" or "multilevel", within QUESO)
 
short precondRequestValue
 the active set request value to use in proposal preconditioning
 
bool logitTransform
 flag indicating user activation of logit transform option More...
 
- Protected Attributes inherited from NonDBayesCalibration
short emulatorType
 the emulator type: NO_EMULATOR, GP_EMULATOR, PCE_EMULATOR, or SC_EMULATOR
 
Model mcmcModel
 Model instance employed in the likelihood function; provides response function values from Gaussian processes, stochastic expansions (PCE/SC), or direct access to simulations (no surrogate option)
 
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 using negative log posterior.
 
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 chainCycles
 number of update cycles for MCMC chain (implemented by restarting of short chains)
 
int randomSeed
 random seed for MCMC process
 
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 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
 
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
 
String exportMCMCFilename
 output filename for the MCMC chain
 
short exportMCMCFormat
 output formatting options for MCMC export
 
short filteredMCMCFormat
 
- Protected Attributes inherited from NonDCalibration
RealVector expStdDeviations
 1 or numFunctions standard deviations
 
bool calibrationData
 flag indicating whether there is calibration data present
 
size_t numExperiments
 number of experiments to read from data file
 
size_t numExpConfigVars
 number of columns in data file which are state variables
 
StringArray varianceTypesRead
 how many sigmas to read from the data file (1 or numFunctions)
 
ExperimentData expData
 Container for experimental data to which to calibrate model.
 
size_t numTotalCalibTerms
 Total number of calibration terms, after replicates/interpolation.
 
- Protected Attributes inherited from NonD
NonDprevNondInstance
 pointer containing previous value of nondInstance
 
Pecos::ProbabilityTransformation natafTransform
 Nonlinear variable transformation that encapsulates the required data for performing transformations from X -> Z -> U and back.
 
size_t numContDesVars
 number of continuous design variables (modeled using uniform distribution for All view modes)
 
size_t numDiscIntDesVars
 number of discrete integer design variables (modeled using discrete histogram distributions for All view modes)
 
size_t numDiscStringDesVars
 number of discrete string design variables (modeled using discrete histogram distributions for All view modes)
 
size_t numDiscRealDesVars
 number of discrete real design variables (modeled using discrete histogram distributions for All view modes)
 
size_t numDesignVars
 total number of design variables
 
size_t numContStateVars
 number of continuous state variables (modeled using uniform distribution for All view modes)
 
size_t numDiscIntStateVars
 number of discrete integer state variables (modeled using discrete histogram distributions for All view modes)
 
size_t numDiscStringStateVars
 number of discrete string state variables (modeled using discrete histogram distributions for All view modes)
 
size_t numDiscRealStateVars
 number of discrete real state variables (modeled using discrete histogram distributions for All view modes)
 
size_t numStateVars
 total number of state variables
 
size_t numNormalVars
 number of normal uncertain variables (native space)
 
size_t numLognormalVars
 number of lognormal uncertain variables (native space)
 
size_t numUniformVars
 number of uniform uncertain variables (native space)
 
size_t numLoguniformVars
 number of loguniform uncertain variables (native space)
 
size_t numTriangularVars
 number of triangular uncertain variables (native space)
 
size_t numExponentialVars
 number of exponential uncertain variables (native space)
 
size_t numBetaVars
 number of beta uncertain variables (native space)
 
size_t numGammaVars
 number of gamma uncertain variables (native space)
 
size_t numGumbelVars
 number of gumbel uncertain variables (native space)
 
size_t numFrechetVars
 number of frechet uncertain variables (native space)
 
size_t numWeibullVars
 number of weibull uncertain variables (native space)
 
size_t numHistogramBinVars
 number of histogram bin uncertain variables (native space)
 
size_t numPoissonVars
 number of Poisson uncertain variables (native space)
 
size_t numBinomialVars
 number of binomial uncertain variables (native space)
 
size_t numNegBinomialVars
 number of negative binomial uncertain variables (native space)
 
size_t numGeometricVars
 number of geometric uncertain variables (native space)
 
size_t numHyperGeomVars
 number of hypergeometric uncertain variables (native space)
 
size_t numHistogramPtIntVars
 number of histogram point integer uncertain variables (native space)
 
size_t numHistogramPtStringVars
 number of histogram point string uncertain variables (native space)
 
size_t numHistogramPtRealVars
 number of histogram point real uncertain variables (native space)
 
size_t numContIntervalVars
 number of continuous interval uncertain variables (native space)
 
size_t numDiscIntervalVars
 number of discrete interval uncertain variables (native space)
 
size_t numDiscSetIntUncVars
 number of discrete integer set uncertain variables (native space)
 
size_t numDiscSetStringUncVars
 number of discrete integer set uncertain variables (native space)
 
size_t numDiscSetRealUncVars
 number of discrete real set uncertain variables (native space)
 
size_t numContAleatUncVars
 total number of continuous aleatory uncertain variables (native space)
 
size_t numDiscIntAleatUncVars
 total number of discrete integer aleatory uncertain variables (native space)
 
size_t numDiscStringAleatUncVars
 total number of discrete string aleatory uncertain variables (native space)
 
size_t numDiscRealAleatUncVars
 total number of discrete real aleatory uncertain variables (native space)
 
size_t numAleatoryUncVars
 total number of aleatory uncertain variables (native space)
 
size_t numContEpistUncVars
 total number of continuous epistemic uncertain variables (native space)
 
size_t numDiscIntEpistUncVars
 total number of discrete integer epistemic uncertain variables (native space)
 
size_t numDiscStringEpistUncVars
 total number of discrete string epistemic uncertain variables (native space)
 
size_t numDiscRealEpistUncVars
 total number of discrete real epistemic uncertain variables (native space)
 
size_t numEpistemicUncVars
 total number of epistemic uncertain variables (native space)
 
size_t numUncertainVars
 total number of uncertain variables (native space)
 
bool epistemicStats
 flag for computing interval-type metrics instead of integrated metrics If any epistemic variables are active in a metric evaluation, then this flag is set.
 
RealMatrix momentStats
 moments of response functions (mean, std deviation, skewness, and kurtosis calculated in compute_moments()), 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
 
size_t miPLIndex
 index for the active ParallelLevel within ParallelConfiguration::miPLIters
 
- 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
 
int maxIterations
 maximum number of iterations for the iterator
 
int maxFunctionEvals
 maximum number of fn evaluations for the iterator
 
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)
 
SizetArray primaryACVarMapIndices
 "primary" all continuous variable mapping indices flowed down from higher level iteration
 
SizetArray primaryADIVarMapIndices
 "primary" all discrete int variable mapping indices flowed down from higher level iteration
 
SizetArray primaryADSVarMapIndices
 "primary" all discrete string variable mapping indices flowed down from higher level iteration
 
SizetArray primaryADRVarMapIndices
 "primary" all discrete real variable mapping indices flowed down from higher level iteration
 
ShortArray secondaryACVarMapTargets
 "secondary" all continuous variable mapping targets flowed down from higher level iteration
 
ShortArray secondaryADIVarMapTargets
 "secondary" all discrete int variable mapping targets flowed down from higher level iteration
 
ShortArray secondaryADSVarMapTargets
 "secondary" all discrete string variable mapping targets flowed down from higher level iteration
 
ShortArray secondaryADRVarMapTargets
 "secondary" all discrete real variable mapping targets flowed down from higher level iteration
 
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
 
ResultsNames resultsNames
 valid names for iterator results
 

Private Attributes

boost::shared_ptr
< QUESO::EnvOptionsValues > 
envOptionsValues
 options for setting up the QUESO Environment
 
boost::shared_ptr
< QUESO::FullEnvironment > 
quesoEnv
 top-level QUESO Environment
 
boost::shared_ptr
< QUESO::VectorSpace
< QUESO::GslVector,
QUESO::GslMatrix > > 
paramSpace
 QUESO parameter space based on number of calibrated parameters.
 
boost::shared_ptr
< QUESO::BoxSubset
< QUESO::GslVector,
QUESO::GslMatrix > > 
paramDomain
 QUESO parameter domain: hypercube based on min/max values.
 
boost::shared_ptr
< QUESO::GslVector > 
paramInitials
 initial parameter values at which to start chain
 
boost::shared_ptr
< QUESO::GslMatrix > 
proposalCovMatrix
 proposal covariance for DRAM
 
boost::shared_ptr
< QUESO::SipOptionsValues > 
calIpOptionsValues
 general inverse problem options
 
boost::shared_ptr
< QUESO::MhOptionsValues > 
calIpMhOptionsValues
 MH-specific inverse problem options.
 
boost::shared_ptr
< QUESO::GenericScalarFunction
< QUESO::GslVector,
QUESO::GslMatrix > > 
likelihoodFunctionObj
 
boost::shared_ptr
< QUESO::BaseVectorRV
< QUESO::GslVector,
QUESO::GslMatrix > > 
priorRv
 
boost::shared_ptr
< QUESO::GenericVectorRV
< QUESO::GslVector,
QUESO::GslMatrix > > 
postRv
 
boost::shared_ptr
< QUESO::StatisticalInverseProblem
< QUESO::GslVector,
QUESO::GslMatrix > > 
inverseProb
 
RealVectorArray uniqueSamples
 container for aggregating unique MCMC sample points collected across multiple (restarted) chains
 
std::map< Real, QUESO::GslVector > bestSamples
 container for managing best MCMC samples (points and associated log posterior) collected across multiple (restarted) chains
 
RealVectorArray prevCoeffs
 cache previous expansion coefficients for assessing convergence of emulator refinement process
 
RealVector mapSoln
 store MAP estimate for warm-starting pre-solves
 

Static Private Attributes

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

Additional Inherited Members

- Static Public Member Functions inherited from NonDBayesCalibration
static void get_positive_definite_covariance_from_hessian (const RealSymMatrix &hessian, const RealMatrix &prior_chol_fact, RealSymMatrix &covariance, short output_lev)
 Compute the proposal covariance C based on low-rank approximation to the prior-preconditioned misfit Hessian.
 
- Static Protected Attributes inherited from NonDBayesCalibration
static NonDBayesCalibrationnonDBayesInstance
 Pointer to current class instance for use in static callback functions.
 

Detailed Description

Bayesian inference using the QUESO library from UT Austin.

This class provides a wrapper to the QUESO library developed as part of the Predictive Science Academic Alliance Program (PSAAP), specifically the PECOS (Predictive Engineering and Computational Sciences) Center at UT Austin. The name QUESO stands for Quantification of Uncertainty for Estimation, Simulation, and Optimization.

Constructor & Destructor Documentation

NonDQUESOBayesCalibration ( 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 Dakota::abort_handler(), NonDCalibration::calibrationData, NonDQUESOBayesCalibration::init_queso_environment(), and NonDBayesCalibration::obsErrorMultiplierMode.

Member Function Documentation

void core_run ( )
protectedvirtual
void print_results ( std::ostream &  s)
protectedvirtual
void aggregate_acceptance_chain ( size_t  cycle_num)
protected
void prior_proposal_covariance ( )
protected

use covariance of prior distribution for setting proposal covariance

Must be called after paramMins/paramMaxs set above

References NonD::natafTransform, Analyzer::numContinuousVars, Iterator::outputLevel, NonDQUESOBayesCalibration::proposalCovMatrix, and NonDBayesCalibration::standardizedSpace.

Referenced by NonDQUESOBayesCalibration::core_run().

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 QUESO::GslMatrix* in proposalCovMatrix with the covariance.

References Analyzer::numContinuousVars, NonDQUESOBayesCalibration::proposalCovMatrix, and NonDBayesCalibration::standardizedSpace.

Referenced by NonDQUESOBayesCalibration::core_run().

void set_ip_options ( )
protected

set inverse problem options calIpOptionsValues common to all solvers

set inverse problem options common to all solvers

References NonDQUESOBayesCalibration::calIpOptionsValues.

Referenced by NonDQUESOBayesCalibration::init_queso_solver().

Member Data Documentation

bool logitTransform
protected

flag indicating user activation of logit transform option

this option is useful for preventing rejection or resampling for out-of-bounds samples by transforming bounded domains to [-inf,inf].

Referenced by NonDQUESOBayesCalibration::set_mh_options().


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