queso-0.57.1
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![]() ![]() ![]() | Class for handling array samples (arrays of scalar sequences) |
![]() ![]() ![]() | Class representing a subset of a vector space shaped like a hypercube |
![]() ![]() ![]() | A templated class representing the concatenation of two vector subsets |
![]() ![]() ![]() | A class for handling scalar functions which image is a constant (real number) |
![]() ![]() ![]() | A class for handling vector functions which image is constant |
![]() ![]() ![]() | A templated class representing the discrete vector subsets |
![]() ![]() ![]() | A class for handling generic scalar functions |
![]() ![]() ![]() | A class for handling generic vector functions |
![]() ![]() ![]() | A templated class representing the intersection of two vector sets |
![]() ![]() ![]() | A templated (base) class for handling scalar functions |
![]() ![]() ![]() | A class for handling Bayesian joint PDFs |
![]() ![]() ![]() | A templated class for synchronizing the calls of scalar functions (BaseScalarFunction and derived classes) |
![]() ![]() ![]() | Class for handling scalar samples |
![]() ![]() ![]() | Class for handling vector samples (sequence of vectors) |
![]() ![]() ![]() | A templated (base) class for handling vector functions |
![]() ![]() ![]() | A templated class for synchronizing the calls of vector-valued functions |
![]() ![]() ![]() | Base class for handling vector and array samples (sequence of vectors or arrays) |
![]() ![]() ![]() | A class representing a vector space |
![]() ![]() ![]() | A templated class for handling sets |
![]() ![]() ![]() | A templated class for handling subsets |
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![]() ![]() ![]() | TODO: Base class for basic PDFs (via either GSL or Boost) |
![]() ![]() ![]() | TODO: Base class for basic PDFs using Boost library |
![]() ![]() ![]() | Base class for basic PDFs using C++ math functions |
![]() ![]() ![]() | TODO: Base class for basic PDFs using Gsl library |
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![]() ![]() ![]() | A class for partitioning vectors and matrices |
![]() ![]() ![]() | Struct for handling data input and output from files |
![]() ![]() ![]() | This (virtual) class sets up the environment underlying the use of the QUESO library by an executable |
![]() ![]() ![]() | This class sets up the environment underlying the use of the QUESO library by an executable |
![]() ![]() ![]() | This class sets up the full environment underlying the use of the QUESO library by an executable |
![]() ![]() ![]() | This class provides a suite options one can pass to a QUESO environment |
![]() ![]() ![]() | This class reads options one can pass to a QUESO environment through an input file |
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![]() ![]() ![]() | Abstract base class for function objects |
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![]() ![]() ![]() | Class for representing block matrices using GSL library |
![]() ![]() ![]() | Class for matrix operations using GSL library |
![]() ![]() ![]() | A base class for handling optimisation of scalar functions |
![]() ![]() ![]() | Class for vector operations using GSL library |
![]() ![]() ![]() | Class defining infinite dimensional Gaussian measures |
![]() ![]() ![]() | Abstract class representing the likelihood. Users must subclass this |
![]() ![]() ![]() | Class representing the infinite dimensional Markov chain Monte Carlo sampler |
![]() ![]() ![]() | This class defines the options that specify the behaviour of the MCMC sampler |
![]() ![]() ![]() | Abstract base class for infinite dimensional measures |
![]() ![]() ![]() | Function objects using libMesh for the backend |
![]() ![]() ![]() | Class describing negative Laplacian operator using libmesh backend |
![]() ![]() ![]() | Abstract base class for operator objects using libmesh in the backend |
![]() ![]() ![]() | A class for partitioning vectors and matrices |
![]() ![]() ![]() | Class for matrix operations (virtual) |
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![]() ![]() ![]() | The QUESO MPI Communicator Class |
![]() ![]() ![]() | Abstract base class for operator objects. Operators are assumed to be symmetric and positive-definite |
![]() ![]() ![]() | A base class for handling optimisation of scalar functions |
![]() ![]() ![]() | Object to monitor convergence of optimizers |
![]() ![]() ![]() | This class provides options for a Optimizer |
![]() ![]() ![]() | Class for random number generation (base class for either GSL or Boost RNG) |
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![]() ![]() ![]() | Class for matrix operations using Teuchos (Trilinos) |
![]() ![]() ![]() | Class for vector operations using Teuchos (Trilinos) |
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![]() ![]() ![]() | Class for vector operations (virtual) |
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![]() ![]() ![]() | This class defines the options that specify the behaviour of the Gaussian process emulator |
![]() ![]() ![]() | Class for one-dimensional functions |
![]() ![]() ![]() | Class for generic one-dimensional functions |
![]() ![]() ![]() | Class for constant one-dimensional functions |
![]() ![]() ![]() | Class for linear one-dimensional functions |
![]() ![]() ![]() | Class for piecewise-linear one-dimensional functions |
![]() ![]() ![]() | Class for one-dimensional quadratic functions |
![]() ![]() ![]() | Class for one-dimensional sampled functions |
![]() ![]() ![]() | Class for multiplication of a one-dimensional function by a scalar |
![]() ![]() ![]() | Class for multiplication of a one-dimensional function by another |
![]() ![]() ![]() | Class for addition of a one-dimensional function with another |
![]() ![]() ![]() | Class for one-dimensional Lagrange polynomials |
![]() ![]() ![]() | Class for Lagrange polynomial basis |
![]() ![]() ![]() | Base class for one-dimensional quadrature rules (numerical integration of functions) |
![]() ![]() ![]() | Class for one-dimensional generic quadrature rules (numerical integration of functions) |
![]() ![]() ![]() | Class for Legendre-Gauss quadrature rule for one-dimensional functions |
![]() ![]() ![]() | Class for Hermite-Gauss quadrature rule for one-dimensional functions |
![]() ![]() ![]() | Class for first type Chebyshev-Gauss quadrature rule for one-dimensional functions |
![]() ![]() ![]() | Class for second type Chebyshev-Gauss quadrature rule for one-dimensional functions |
![]() ![]() ![]() | Class for handling arrays of generic data |
![]() ![]() ![]() | Class to accommodate arrays of one-dimensional grid |
![]() ![]() ![]() | Class to accommodate arrays of one-dimensional tables |
![]() ![]() ![]() | Class for reading ASCII values from a table in a file |
![]() ![]() ![]() | Base class for quadrature rules |
![]() ![]() ![]() | Class for a Fast Fourier Transform (FFT) algorithm |
![]() ![]() ![]() | Numerical integration using Monte Carlo |
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![]() ![]() ![]() | Base class for multi-dimensional quadrature rules |
![]() ![]() ![]() | Base class for accommodating one-dimensional grids |
![]() ![]() ![]() | Class for accommodating standard one-dimensional grids |
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![]() ![]() ![]() | Numerical quadrature using a tensor product of Base1DQuadrature rules |
![]() ![]() ![]() | Class for accommodating uniform one-dimensional grids |
![]() ![]() ![]() | This base class allows the representation of a transition kernel |
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![]() ![]() ![]() | A class for handling Beta joint PDFs |
![]() ![]() ![]() | A class for handling sampling from a Beta probability density distribution |
![]() ![]() ![]() | A class representing a vector RV constructed via Beta distribution |
![]() ![]() ![]() | A class for handling concatenated PDFs |
![]() ![]() ![]() | A class for handling sampling from concatenated probability density distributions |
![]() ![]() ![]() | A class representing concatenated vector RVs |
![]() ![]() ![]() | A class for exponential covariance matrices |
![]() ![]() ![]() | A class for exponential covariances |
![]() ![]() ![]() | A templated class for a finite distribution |
![]() ![]() ![]() | A class for handling Gamma joint PDFs |
![]() ![]() ![]() | A class for handling sampling from a Gamma probability density distribution |
![]() ![]() ![]() | A class representing a vector RV constructed via Gamma distribution |
![]() ![]() ![]() | A class for handling Gaussian joint PDFs |
![]() ![]() ![]() | A class representing a Gaussian likelihood with block-diagonal covariance matrix |
![]() ![]() ![]() | A class representing a Gaussian likelihood with block-diagonal covariance matrix |
![]() ![]() ![]() | A class that represents a Gaussian likelihood with diagonal covariance matrix |
![]() ![]() ![]() | A class that represents a Gaussian likelihood with full covariance |
![]() ![]() ![]() | A class that represents a Gaussian likelihood with full covariance and random coefficient |
![]() ![]() ![]() | A class that represents a Gaussian likelihood with scalar covariance |
![]() ![]() ![]() | TODO: A class for handling Gaussian CDFs |
![]() ![]() ![]() | TODO: A class for handling Gaussian MDFs |
![]() ![]() ![]() | A class for handling sampling from Gaussian probability density distributions |
![]() ![]() ![]() | A class representing a Gaussian vector RV |
![]() ![]() ![]() | A class for handling generic joint PDFs |
![]() ![]() ![]() | A class for generic covariance matrices |
![]() ![]() ![]() | A class for generic covariances |
![]() ![]() ![]() | A class for handling generic vector CDFs |
![]() ![]() ![]() | A class for handling generic MDFs of vector functions |
![]() ![]() ![]() | A class for handling sampling from generic probability density distributions |
![]() ![]() ![]() | A templated class for handling generic vector RVs |
![]() ![]() ![]() | This class allows the representation of a transition kernel with Hessians |
![]() ![]() ![]() | A class for handling Inverse Gamma joint PDFs |
![]() ![]() ![]() | A class for handling sampling from an Inverse Gamma probability density distribution |
![]() ![]() ![]() | A class representing a vector RV constructed via Inverse Gamma distribution |
![]() ![]() ![]() | A class for handling hybrid (transformed) Gaussians with bounds |
![]() ![]() ![]() | A class for handling sampling from (transformed) Gaussian probability density distributions with bounds |
![]() ![]() ![]() | A class representing a (transformed) Gaussian vector RV with bounds |
![]() ![]() ![]() | A class for handling jeffreys joint PDFs |
![]() ![]() ![]() | A class for handling sampling from a jeffreys probability density distribution |
![]() ![]() ![]() | A class representing a jeffreys vector RV |
![]() ![]() ![]() | A templated (base) class for handling joint PDFs |
![]() ![]() ![]() | Base class for canned Gaussian likelihoods |
![]() ![]() ![]() | A class for handling Log-Normal joint PDFs |
![]() ![]() ![]() | A class for handling sampling from a Log-Normal probability density distribution |
![]() ![]() ![]() | A class representing a LogNormal vector RV |
![]() ![]() ![]() | A templated class that represents a Markov Chain |
![]() ![]() ![]() | A templated (base) class to accommodate covariance matrix of (random) vector functions |
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![]() ![]() ![]() | This class allows the representation of the MALA transition kernel with a scaled covariance matrix for the purposes of delayed rejection |
![]() ![]() ![]() | A struct that represents a Metropolis-Hastings sample |
![]() ![]() ![]() | A templated class that represents a Metropolis-Hastings generator of samples |
![]() ![]() ![]() | This class provides options for the Metropolis-Hastings generator of samples if no input file is available |
![]() ![]() ![]() | This class reads the options for the Metropolis-Hastings generator of samples from an input file |
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![]() ![]() ![]() | A templated class that represents a Multilevel generator of samples |
![]() ![]() ![]() | This class provides options for each level of the Multilevel sequence generator if no input file is available |
![]() ![]() ![]() | This class provides options for the Multilevel sequence generator if no input file is available |
![]() ![]() ![]() | A templated class for model validation of the example validationPyramid |
![]() ![]() ![]() | A templated class that implements a Monte Carlo generator of samples |
![]() ![]() ![]() | This class provides options for the Monte Carlo sequence generator if no input file is available |
![]() ![]() ![]() | This class reads the options for the Monte Carlo sequence generator from an input file |
![]() ![]() ![]() | A class for handling a powered joint PDFs |
![]() ![]() ![]() | A class for handling sampled CDFs |
![]() ![]() ![]() | A class for handling sampled vector CDFs |
![]() ![]() ![]() | A class for handling sampled vector MDFs |
![]() ![]() ![]() | A templated (base) class for handling CDFs |
![]() ![]() ![]() | A templated (base) class to accommodate scalar covariance functions (of random variables) |
![]() ![]() ![]() | A class for handling scalar Gaussian random fields (GRF) |
![]() ![]() ![]() | This class allows the representation of a transition kernel with a scaled covariance matrix |
![]() ![]() ![]() | A class for handling sequential draws (sampling) from probability density distributions |
![]() ![]() ![]() | This templated class represents a Statistical Forward Problem |
![]() ![]() ![]() | This class provides options for a Statistical Forward Problem if no input file is available |
![]() ![]() ![]() | This class reads option values for a Statistical Forward Problem from an input file |
![]() ![]() ![]() | This templated class represents a Statistical Inverse Problem |
![]() ![]() ![]() | This class provides options for a Statistical Inverse Problem if no input file is available |
![]() ![]() ![]() | This class reads option values for a Statistical Inverse Problem from an input file |
![]() ![]() ![]() | A class for handling standard CDFs |
![]() ![]() ![]() | This class represents a transition kernel with a scaled covariance matrix on hybrid bounded/unbounded state spaces |
![]() ![]() ![]() | A class for handling uniform joint PDFs |
![]() ![]() ![]() | A class for handling sampling from a Uniform probability density distribution |
![]() ![]() ![]() | A class representing a uniform vector RV |
![]() ![]() ![]() | A templated class for validation cycle of the examples validationCycle and validationCycle2 |
![]() ![]() ![]() | A templated (base) class for handling CDFs of vector functions |
![]() ![]() ![]() | A class for handling vector Gaussian random fields (GRF) |
![]() ![]() ![]() | A templated (base) class for handling MDFs of vector functions |
![]() ![]() ![]() | A templated (base) class for handling sampling from vector RVs |
![]() ![]() ![]() | A templated base class for handling vector RV |
![]() ![]() ![]() | A class for handling Wigner joint PDFs |
![]() ![]() ![]() | A class for handling sampling from a Wigner probability density distribution |
![]() ![]() ![]() | A class representing a vector RV constructed via Wigner distribution |
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![]() ![]() ![]() | Base class for interpolation-based surrogates |
![]() ![]() ![]() | Build interpolation-based surrogate |
![]() ![]() ![]() | Container class for multiple, consistent InterpolationSurrogateData objects |
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![]() ![]() ![]() | Linear Lagrange interpolation surrogate |
![]() ![]() ![]() | Base class for surrogates of models |
![]() ![]() ![]() | Base class for builders of surrogates |
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![]() ![]() | Class for random number generation using Boost library |
![]() ![]() | Class for random number generation using std::random from C++11 |
![]() ![]() | Class for random number generation using GSL library |
![]() ![]() | Definition of a scoped pointer |
![]() ![]() | A templated class that stores statistical options (optionally read from an input file) |
![]() ![]() | Definition of a shared pointer |
![]() ![]() | A templated class that stores default statistical options for a sequence of vectors, e.g. a Markov chain, a Monte Carlo input sequence, or a Monte Carlo output sequence |