boost | |
program_options | |
QUESO | |
ArrayOfSequences | Class for handling array samples (arrays of scalar sequences) |
BoxSubset | Class representing a subset of a vector space shaped like a hypercube |
ConcatenationSubset | A templated class representing the concatenation of two vector subsets |
ConstantScalarFunction | A class for handling scalar functions which image is a constant (real number) |
ConstantVectorFunction | A class for handling vector functions which image is constant |
DiscreteSubset | A templated class representing the discrete vector subsets |
GenericScalarFunction | A class for handling generic scalar functions |
GenericVectorFunction | A class for handling generic vector functions |
IntersectionSubset | A templated class representing the intersection of two vector sets |
BaseScalarFunction | A templated (base) class for handling scalar functions |
ScalarFunctionSynchronizer | A templated class for synchronizing the calls of scalar functions (BaseScalarFunction and derived classes) |
ScalarSequence | Class for handling scalar samples |
SequenceOfVectors | Class for handling vector samples (sequence of vectors) |
BaseVectorFunction | A templated (base) class for handling vector functions |
VectorFunctionSynchronizer | A templated class for synchronizing the calls of vector-valued functions |
BaseVectorSequence | Base class for handling vector and array samples (sequence of vectors or arrays) |
VectorSpace | A class representing a vector space |
VectorSet | A templated class for handling sets |
VectorSubset | A templated class for handling subsets |
BaseInputOptionsParser | |
BasicPdfsBase | TODO: Base class for basic PDFs (via either GSL or Boost) |
BasicPdfsBoost | TODO: Base class for basic PDFs using Boost library |
BasicPdfsGsl | TODO: Base class for basic PDFs using Gsl library |
BoostInputOptionsParser | |
DistArray | A class for partitioning vectors and matrices |
FilePtrSetStruct | Struct for handling data input and output from files |
BaseEnvironment | This (virtual) class sets up the environment underlying the use of the QUESO library by an executable |
EmptyEnvironment | This class sets up the environment underlying the use of the QUESO library by an executable |
FullEnvironment | This class sets up the full environment underlying the use of the QUESO library by an executable |
EnvOptionsValues | This class provides a suite options one can pass to a QUESO environment |
EnvironmentOptions | This class reads options one can pass to a QUESO environment through an input file |
LogicError | |
NotImplemented | |
FileError | |
FunctionBase | Abstract base class for function objects |
FunctionOperatorBuilder | |
GslBlockMatrix | Class for representing block matrices using GSL library |
GslMatrix | Class for matrix operations using GSL library |
GslOptimizer | A base class for handling optimisation of scalar functions |
GslVector | Class for vector operations using GSL library |
InfiniteDimensionalGaussian | Class defining infinite dimensional Gaussian measures |
InfiniteDimensionalLikelihoodBase | Abstract class representing the likelihood. Users must subclass this |
InfiniteDimensionalMCMCSamplerOptions | This class defines the options that specify the behaviour of the MCMC sampler |
InfiniteDimensionalMeasureBase | Abstract base class for infinite dimensional measures |
Map | A class for partitioning vectors and matrices |
Matrix | Class for matrix operations (virtual) |
data_type | |
DataType | |
StandardType | |
MpiComm | The QUESO MPI Communicator Class |
OperatorBase | Abstract base class for operator objects. Operators are assumed to be symmetric and positive-definite |
BaseOptimizer | A base class for handling optimisation of scalar functions |
OptimizerMonitor | Object to monitor convergence of optimizers |
RngBase | Class for random number generation (base class for either GSL or Boost RNG) |
RngBoost | |
RngGsl | |
Vector | Class for vector operations (virtual) |
ExperimentModel | |
EmOptionsValues | |
ExperimentModelOptions | |
ExperimentStorage | |
GcmExperimentInfo | |
GcmJointInfo | |
GcmJointTildeInfo | |
GcmSimulationInfo | |
GcmSimulationTildeInfo | |
GcmTotalInfo | |
GcmZInfo | |
GcmZTildeInfo | |
GPMSAEmulator | |
GPMSAFactory | |
GpmsaComputerModel | |
GcmOptionsValues | |
GpmsaComputerModelOptions | |
GPMSAOptions | This class defines the options that specify the behaviour of the Gaussian process emulator |
SimulationModel | |
SmOptionsValues | |
SimulationModelOptions | |
SimulationStorage | |
Base1D1DFunction | Class for one-dimensional functions |
Generic1D1DFunction | Class for generic one-dimensional functions |
Constant1D1DFunction | Class for constant one-dimensional functions |
Linear1D1DFunction | Class for linear one-dimensional functions |
PiecewiseLinear1D1DFunction | Class for piecewise-linear one-dimensional functions |
Quadratic1D1DFunction | Class for one-dimensional quadratic functions |
Sampled1D1DFunction | Class for one-dimensional sampled functions |
ScalarTimesFunc1D1DFunction | Class for multiplication of a one-dimensional function by a scalar |
FuncTimesFunc1D1DFunction | Class for multiplication of a one-dimensional function by another |
FuncPlusFunc1D1DFunction | Class for addition of a one-dimensional function with another |
LagrangePolynomial1D1DFunction | Class for one-dimensional Lagrange polynomials |
LagrangeBasis1D1DFunction | Class for Lagrange polynomial basis |
Base1DQuadrature | Base class for one-dimensional quadrature rules (numerical integration of functions) |
Generic1DQuadrature | Class for one-dimensional generic quadrature rules (numerical integration of functions) |
UniformLegendre1DQuadrature | Class for Legendre-Gauss quadrature rule for one-dimensional functions |
GaussianHermite1DQuadrature | Class for Hermite-Gauss quadrature rule for one-dimensional functions |
WignerInverseChebyshev1st1DQuadrature | Class for first type Chebyshev-Gauss quadrature rule for one-dimensional functions |
WignerChebyshev2nd1DQuadrature | Class for second type Chebyshev-Gauss quadrature rule for one-dimensional functions |
TwoDArray | Class for handling arrays of generic data |
ArrayOfOneDGrids | Class to accommodate arrays of one-dimensional grid |
ArrayOfOneDTables | Class to accommodate arrays of one-dimensional tables |
AsciiTable | Class for reading ASCII values from a table in a file |
Fft | Class for a Fast Fourier Transform (FFT) algorithm |
BaseOneDGrid | Base class for accommodating one-dimensional grids |
StdOneDGrid | Class for accommodating standard one-dimensional grids |
StreamUtilities | |
UniformOneDGrid | Class for accommodating uniform one-dimensional grids |
BayesianJointPdf | A class for handling Bayesian joint PDFs |
BetaJointPdf | A class for handling Beta joint PDFs |
BetaVectorRealizer | A class for handling sampling from a Beta probability density distribution |
BetaVectorRV | A class representing a vector RV constructed via Beta distribution |
ConcatenatedJointPdf | A class for handling concatenated PDFs |
ConcatenatedVectorRealizer | A class for handling sampling from concatenated probability density distributions |
ConcatenatedVectorRV | A class representing concatenated vector RVs |
ExponentialMatrixCovarianceFunction | A class for exponential covariance matrices |
ExponentialScalarCovarianceFunction | A class for exponential covariances |
FiniteDistribution | A templated class for a finite distribution |
GammaJointPdf | A class for handling Gamma joint PDFs |
GammaVectorRealizer | A class for handling sampling from a Gamma probability density distribution |
GammaVectorRV | A class representing a vector RV constructed via Gamma distribution |
GaussianJointPdf | A class for handling Gaussian joint PDFs |
BaseGaussianLikelihood | Base class for canned Gaussian likelihoods |
GaussianLikelihoodBlockDiagonalCovariance | A class representing a Gaussian likelihood with block-diagonal covariance matrix |
GaussianLikelihoodBlockDiagonalCovarianceRandomCoefficients | A class representing a Gaussian likelihood with block-diagonal covariance matrix |
GaussianLikelihoodDiagonalCovariance | A class that represents a Gaussian likelihood with diagonal covariance matrix |
GaussianLikelihoodFullCovariance | A class that represents a Gaussian likelihood with full covariance |
GaussianLikelihoodFullCovarianceRandomCoefficient | A class that represents a Gaussian likelihood with full covariance and random coefficient |
GaussianLikelihoodScalarCovariance | A class that represents a Gaussian likelihood with scalar covariance |
GaussianVectorCdf | TODO: A class for handling Gaussian CDFs |
GaussianVectorMdf | TODO: A class for handling Gaussian MDFs |
GaussianVectorRealizer | A class for handling sampling from Gaussian probability density distributions |
GaussianVectorRV | A class representing a Gaussian vector RV |
GenericJointPdf | A class for handling generic joint PDFs |
GenericMatrixCovarianceFunction | A class for generic covariance matrices |
GenericScalarCovarianceFunction | A class for generic covariances |
GenericVectorCdf | A class for handling generic vector CDFs |
GenericVectorMdf | A class for handling generic MDFs of vector functions |
GenericVectorRealizer | A class for handling sampling from generic probability density distributions |
GenericVectorRV | A templated class for handling generic vector RVs |
HessianCovMatricesTKGroup | This class allows the representation of a transition kernel with Hessians |
InverseGammaJointPdf | A class for handling Inverse Gamma joint PDFs |
InverseGammaVectorRealizer | A class for handling sampling from an Inverse Gamma probability density distribution |
InverseGammaVectorRV | A class representing a vector RV constructed via Inverse Gamma distribution |
InvLogitGaussianJointPdf | A class for handling hybrid (transformed) Gaussians with bounds |
InvLogitGaussianVectorRealizer | A class for handling sampling from (transformed) Gaussian probability density distributions with bounds |
InvLogitGaussianVectorRV | A class representing a (transformed) Gaussian vector RV with bounds |
JeffreysJointPdf | A class for handling jeffreys joint PDFs |
JeffreysVectorRealizer | A class for handling sampling from a jeffreys probability density distribution |
JeffreysVectorRV | A class representing a jeffreys vector RV |
BaseJointPdf | A templated (base) class for handling joint PDFs |
LogNormalJointPdf | A class for handling Log-Normal joint PDFs |
LogNormalVectorRealizer | A class for handling sampling from a Log-Normal probability density distribution |
LogNormalVectorRV | A class representing a LogNormal vector RV |
MarkovChainPositionData | A templated class that represents a Markov Chain |
BaseMatrixCovarianceFunction | A templated (base) class to accommodate covariance matrix of (random) vector functions |
MHRawChainInfoStruct | A struct that represents a Metropolis-Hastings sample |
MetropolisHastingsSG | A templated class that represents a Metropolis-Hastings generator of samples |
MhOptionsValues | This class provides options for the Metropolis-Hastings generator of samples if no input file is available |
MetropolisHastingsSGOptions | This class reads the options for the Metropolis-Hastings generator of samples from an input file |
ExchangeInfoStruct | |
BalancedLinkedChainControlStruct | |
BalancedLinkedChainsPerNodeStruct | |
UnbalancedLinkedChainControlStruct | |
UnbalancedLinkedChainsPerNodeStruct | |
MLSampling | A templated class that represents a Multilevel generator of samples |
MLSamplingLevelOptions | This class provides options for each level of the Multilevel sequence generator if no input file is available |
MLSamplingOptions | This class provides options for the Multilevel sequence generator if no input file is available |
ModelValidation | A templated class for model validation of the example validationPyramid |
MonteCarloSG | A templated class that implements a Monte Carlo generator of samples |
McOptionsValues | This class provides options for the Monte Carlo sequence generator if no input file is available |
MonteCarloSGOptions | This class reads the options for the Monte Carlo sequence generator from an input file |
PoweredJointPdf | A class for handling a powered joint PDFs |
SampledScalarCdf | A class for handling sampled CDFs |
SampledVectorCdf | A class for handling sampled vector CDFs |
SampledVectorMdf | A class for handling sampled vector MDFs |
BaseScalarCdf | A templated (base) class for handling CDFs |
BaseScalarCovarianceFunction | A templated (base) class to accommodate scalar covariance functions (of random variables) |
ScalarGaussianRandomField | A class for handling scalar Gaussian random fields (GRF) |
ScaledCovMatrixTKGroup | This class allows the representation of a transition kernel with a scaled covariance matrix |
SequentialVectorRealizer | A class for handling sequential draws (sampling) from probability density distributions |
StatisticalForwardProblem | This templated class represents a Statistical Forward Problem |
SfpOptionsValues | This class provides options for a Statistical Forward Problem if no input file is available |
StatisticalForwardProblemOptions | This class reads option values for a Statistical Forward Problem from an input file |
StatisticalInverseProblem | This templated class represents a Statistical Inverse Problem |
SipOptionsValues | This class provides options for a Statistical Inverse Problem if no input file is available |
StatisticalInverseProblemOptions | This class reads option values for a Statistical Inverse Problem from an input file |
StdScalarCdf | A class for handling standard CDFs |
BaseTKGroup | This base class allows the representation of a transition kernel |
TransformedScaledCovMatrixTKGroup | This class represents a transition kernel with a scaled covariance matrix on hybrid bounded/unbounded state spaces |
UniformJointPdf | A class for handling uniform joint PDFs |
UniformVectorRealizer | A class for handling sampling from a Uniform probability density distribution |
UniformVectorRV | A class representing a uniform vector RV |
ValidationCycle | A templated class for validation cycle of the examples validationCycle and validationCycle2 |
BaseVectorCdf | A templated (base) class for handling CDFs of vector functions |
VectorGaussianRandomField | A class for handling vector Gaussian random fields (GRF) |
BaseVectorMdf | A templated (base) class for handling MDFs of vector functions |
BaseVectorRealizer | A templated (base) class for handling sampling from vector RVs |
BaseVectorRV | A templated base class for handling vector RV |
WignerJointPdf | A class for handling Wigner joint PDFs |
WignerVectorRealizer | A class for handling sampling from a Wigner probability density distribution |
WignerVectorRV | A class representing a vector RV constructed via Wigner distribution |
InterpolationSurrogateData | |
InterpolationSurrogateBase | Base class for interpolation-based surrogates |
InterpolationSurrogateBuilder | Build interpolation-based surrogate |
InterpolationSurrogateDataSet | Container class for multiple, consistent InterpolationSurrogateData objects |
InterpolationSurrogateHelper | |
InterpolationSurrogateIOASCII | |
InterpolationSurrogateIOBase | |
LinearLagrangeInterpolationSurrogate | Linear Lagrange interpolation surrogate |
SurrogateBase | Base class for surrogates of models |
SurrogateBuilderBase | Base class for builders of surrogates |
ANNbd_shrink | |
ANNbd_tree | |
ANNbruteForce | |
ANNkd_leaf | |
ANNkd_node | |
ANNkd_split | |
ANNkd_tree | |
ANNkdStats | |
ANNmin_k | |
mk_node | |
ANNorthHalfSpace | |
ANNorthRect | |
ANNpointSet | |
ANNpr_queue | |
pq_node | |
ANNsampStat | |
RngBoost | Class for random number generation using Boost library |
RngGsl | Class for random number generation using GSL library |