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Dakota  Version 6.2
Public Member Functions | Protected Member Functions | Protected Attributes
NonDBayesCalibration Class Reference

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

Inheritance diagram for NonDBayesCalibration:
NonDCalibration NonD Analyzer Iterator NonDDREAMBayesCalibration NonDGPMSABayesCalibration NonDQUESOBayesCalibration

List of all members.

Public Member Functions

 NonDBayesCalibration (ProblemDescDB &problem_db, Model &model)
 standard constructor
 ~NonDBayesCalibration ()
 destructor

Protected Member Functions

void derived_init_communicators (ParLevLIter pl_iter)
 derived class contributions to initializing the communicators associated with this Iterator instance
void derived_set_communicators (ParLevLIter pl_iter)
 derived class contributions to setting the communicators associated with this Iterator instance
void derived_free_communicators (ParLevLIter pl_iter)
 derived class contributions to freeing the communicators associated with this Iterator instance
const Modelalgorithm_space_model () const
void initialize_model ()
 initialize emulator model and probability space transformations

Protected Attributes

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)
Iterator stochExpIterator
 NonDPolynomialChaos or NonDStochCollocation instance for defining a PCE/SC-based mcmcModel.
int numSamples
 number of samples in the chain (e.g. number of MCMC samples)
int chainCycles
 number of update cycles for MCMC chain (implemented by restarting of short chains)
int randomSeed
 random seed for MCMC process
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

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 ( ProblemDescDB problem_db,
Model model 
)

Member Function Documentation

const Model & algorithm_space_model ( ) const [inline, protected, virtual]

default definition that gets redefined in selected derived Minimizers

Reimplemented from Analyzer.

References NonDBayesCalibration::mcmcModel.


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