Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 12]
  QUESO | |
   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 |
| EnvOptionsValues | This class provides a suite options one can pass to a QUESO environment |
| 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 |
| 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 |
| BaseScalarFunction | A templated (base) class for handling scalar functions |
| 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) |
| 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) |
| 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 |
| UniformOneDGrid | Class for accommodating uniform one-dimensional grids |
| 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 |
| 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 |
| 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 |
| RngBoost | Class for random number generation using Boost library |
| RngGsl | Class for random number generation using GSL library |