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Showing 1 to 14 of 14 for “"Monte Carlo integration"”.

  1. Monte Carlo integration in discrete undirected probabilistic models

    … work in and contributions to the field of Monte Carlo sampling for undirected graphical models, a class of statistical model commonly used in machine learning, computer vision, and spatial statistics; the aim is to be able to use the methodology and resultant samples to estimate integrals …

    ubc Repository record for Monte Carlo integration in discrete undirected probabilistic models (opens in a new tab)

  2. Importance Resampling for Global Illumination

    This thesis develops a generalized form of Monte Carlo integration called Resampled Importance Sampling. It is based on the importance resampling sample generation technique. Resampled Importance Sampling can lead to significant variance reduction over standard Monte Carlo integration for common …

    byu Repository record for Importance Resampling for Global Illumination (opens in a new tab)

  3. Monte Carlo Studies of Momentum Distributions and Longitudinal Response Functions of a = 3 and 4 Nuclei

    … obtained with this method are reported. The Monte Carlo integration technique, also briefly reviewed, is used to evaluate various expectation values, and is generalized to calculate quantities such as momentum distributions and transition probabilities.

    uiuc Repository record for Monte Carlo Studies of Momentum Distributions and Longitudinal Response Functions of a = 3 and 4 Nuclei (opens in a new tab)

  4. An embedded domain specific sampling language for Monte Carlo rendering

    Implementing Monte Carlo integration requires significant domain expertise. While simple algorithms, such as unidirectional path tracing, are relatively forgiving, more complex algorithms, such as bidirectional path tracing or Metropolis methods, are notoriously difficult to implement correctly. We …

    mit Repository record for An embedded domain specific sampling language for Monte Carlo rendering (opens in a new tab)

  5. Efficient numerical methods for solving the Boltzmann equation for low-speed flows

    … equation is performed using a quasi-random Monte Carlo integration approach for faster convergence. In addition, interpolation is used to reduce the effect of discretization errors. We find that cubic interpolation leads to accurate solutions which exhibit excellent conservation properties, …

    mit Repository record for Efficient numerical methods for solving the Boltzmann equation for low-speed flows (opens in a new tab)

  6. Techniques for stochastic simulation of complex electromagnetic and circuit systems with uncertainties

    … results are shown comparing the new solver to Monte Carlo techniques using a commercial circuit solver. The simulator is then used to simulate several transmission line problems, including single- and multi-conductor, crosstalk, and coupled-line on a printed circuit board substrate with …

    uiuc Repository record for Techniques for stochastic simulation of complex electromagnetic and circuit systems with uncertainties (opens in a new tab)

  7. Lattice QCD determination of weak decays of B mesons

    … in future calculations. This is done using Monte-Carlo integration to evaluate integrals from diagrams generated using automated lattice perturbation theory in background field gauge in order to match the coefficients of the effective action between the lattice and the continuum.

    cambridge Repository record for Lattice QCD determination of weak decays of B mesons (opens in a new tab)

  8. Monte Carlo approaches to the protein folding problem

    … volume of a polymer is defined and calculated by Monte Carlo integration. The excluded volume for a polymer with another polymer of the same length scales as N1.74. These results agree with theoretical predictions about the behavior of polymers in the dilute solution regime. The conformation of a …

    texas Repository record for Monte Carlo approaches to the protein folding problem (opens in a new tab)

  9. Estimation of astronomical images from the bispectrum of atmospherically distorted infrared data.

    … are developed. The models are evaluated by Monte Carlo integration and the results are compared to sample estimates of the same quantities obtained from simulated data. For comparison, the same sample quantities are computed from observed data. The bispectrum is shown to be useful for …

    arizona-thes Repository record for Estimation of astronomical images from the bispectrum of atmospherically distorted infrared data. (opens in a new tab)

  10. Monte Carlo studies of momentum distributions and longitudinal response functions of A=3 and 4 nuclei

    … obtained with this method are reported. The Monte Carlo integration technique, also briefly reviewed, is used to evaluate various expectation values, and is generalized to calculate quantities such as momentum distributions and transition probabilities. The longitudinal structure function and …

    uiuc Repository record for Monte Carlo studies of momentum distributions and longitudinal response functions of A=3 and 4 nuclei (opens in a new tab)

  11. Statistical mechanical theory of equilibrium structure and miscibility of polymer nanocomposites: effects of polymer chemical heterogeneity and architecture, and nanoparticle surface corrugation and softness

    … with surface fluctuations and fuzziness. Monte Carlo integration and other computational techniques have been developed to compute the effective interactions between such particles. The morphologically diverse particles introduce additional length scales, making the physics non-monotonic, …

    uiuc Repository record for Statistical mechanical theory of equilibrium structure and miscibility of polymer nanocomposites: effects of polymer chemical heterogeneity and architecture, and nanoparticle surface corrugation and softness (opens in a new tab)

  12. Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle

    … from the number of estimable parameters. Monte Carlo integration with importance sampling was shown to help approximate the one-part criterion. Simulations assessed model selection under various beta regression settings for the two-part criterion. Results indicated that the two-part …

    ku Repository record for Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle (opens in a new tab)

  13. Advancements in Monte Carlo many body methods

    … electron binding energies are obtained through Monte Carlo integration. The motivation to create stochastic implementations of MBPT and MBGF was to prioritize parallelizability, as Monte Carlo integration is trivially parallel, so that modern supercomputer may be used effectively in the …

    uiuc Repository record for Advancements in Monte Carlo many body methods (opens in a new tab)

  14. The power of random information for numerical approximation and integration

    … of random information for approximation and integration problems. In the first problem considered, information given by linear functionals is used to recover vectors, in particular from generalized ellipsoids. This is related to the approximation of diagonal operators which are important …

    passau-thes Repository record for The power of random information for numerical approximation and integration (opens in a new tab)