Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 62 for “"Monte Carlo Sampling"”.
-
Efficient Monte Carlo Sampling of Lattice Field Theories
Monte Carlo Sampling of Quantum Field Theories suffers from many inefficiencies. These inefficiencies, among other things, make determination of the QCD phase diagram and calculation of the correlation functions numerically difficult. As a step towards eventually overcoming these issues, the …
-
On Grouped Observation Level Interaction and a Big Data Monte Carlo Sampling Algorithm
… with Multidimensional Scaling and a big data Monte Carlo sampling algorithm named Batched Permutation Sampler. These two algorithms are designed to enhance the capability of generating meaningful insights and utilizing massive datasets, respectively.
-
Entropy functions and rare events in disordered systems by transfer matrix calculations and Monte Carlo sampling
… thesis treats problems from bioinformatics with Monte Carlo methods from statistical physics. Methods to compare molecular sequences (sequence alignment) make use of statistical tests to assess the significance of observed similarities. Distributions of optimal alignment scores over random …
-
Iterative Monte Carlo for quantum dynamics
… of iterative propagation with the features of Monte Carlo sampling. The stepwise evaluation of the path integral circumvents the growth of statistical error with time and the use of importance sampling leads to a favorable scaling of required grid points with the number of particles. Three …
-
Stochastic numerical approximation approaches for estimation of traffic volume under travel demand uncertainties
… applied to the problem and compared to Monte Carlo sampling. Performance of constructed interpolant was evaluated through output distribution recovery , statistical moment estimation, and computation time comparisons. Ability of sparse grid to efficiently handle demand uncertainties …
-
Models for Yield Estimation of Multichip Module Ceramic Substrates
… general defect shapes. Yield is estimated using Monte Carlo sampling methods. Finally, we present methods for estimating the parameters of the yield models using observed data from the manufacturing process.
-
Model Reduction and Domain Decomposition Methods for Uncertainty Quantification
… a Stochastic Elliptic Equation (SEE) via a Monte Carlo sampling method. The approach takes advantage of a lower stochastic dimension at the subdomain level to construct a PC expansion of a reduced linear system that is later used to compute samples of the solution. Thus, the approach …
-
Deciding among models : a decision-theoretic view of model complexity
… Finally, it presents original results applying Monte Carlo sampling to a drilling decision scenario and to a one-dimensional reservoir model where a cylindrical oil field is represented by different numbers of cells and the results compared.
-
Modern Bayesian Object Tracking: Challenges and Solutions
… applications based on the efficient design of Monte Carlo sampling methods, which sets us apart from the existing techniques that are based on the Kalman filter or other recursive closed-form Gaussian mixture filter implementations with approximations and heuristic design. The Monte Carlo …
-
An integrated performance model for high temperature gas cooled reactor coated particle fuel
… irradiation histories, and the incorporation of Monte Carlo sampling to account for the statistical variation of particle properties.
-
Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking
… theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for …
-
Bayesian source inversion of microseismic events
… be used to search the source space, including Monte Carlo random sampling and Markov chain Monte Carlo sampling. Relative information between co-located events may be used as an extension to the framework, improving the constraint on the source. The double-couple source is the commonly assumed …
-
Probabilistic Models and Algorithmic Analysis of Network Problems
… using relaxed multi-criterial approximation and Monte Carlo sampling. We provide theoretically proven properties and supportive empirical results.
-
Optimal Bayesian experimental design in the presence of model error
… gain from proposed experimental designs. Monte Carlo sampling is used to evaluate the expected information gain, and stochastic approximation algorithms make optimization feasible for computationally intensive and high-dimensional problems. A key aspect of our framework is the introduction …
-
Inference Plans for Hybrid Probabilistic Inference
… systems to combine symbolic exact inference and Monte Carlo sampling to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are represented symbolically and variables that are represented by sampled values, and in …
-
Improved quarry design using deterministic and probabilistic techniques
… high wall designs. Reliability analysis using Monte Carlo sampling minimizes uncertainty and allows the use of all available data in a stability evaluation. E~tensive "help" menus are incorporated into the program. The "help" menus include ranges of physical properties such as cohesion and …
-
Optimal approximations of coupling in multidisciplinary models
… the discipline couplings. An adaptive sequential Monte Carlo sampling-based technique is used to efficiently search the combinatorial model space of different discipline couplings. Finally, an algorithm for optimal model selection is presented and combined with three tractable approaches to …
-
Nonadiabatic electron transfer in the condensed phase, via semiclassical and Langevin equation approach
… Lastly, we tested the feasibility of using Monte Carlo sampling to compute the memory kernel from the spin-boson system and proposed a smoothing technique to reduce the number of sampling points.
-
Probabilistic aerothermal design of compressor airfoils
… gradients are approximated using low-resolution Monte Carlo sampling. Test airfoils were optimized both deterministically and probabilistically and then analyzed probabilistically to account for geometric variability. Probabilistically redesigned airfoils exhibited reductions in mean loss of up …
-
Shaping light-matter interactions for free-electron radiation and photonic computing
… free-electron radiation and implementing Monte Carlo sampling algorithms in photonic circuits. We present a framework to model, tailor, and enhance radiation from free electrons and other high-energy particles interacting with nanophotonic structures. We then describe the building of a …
Page 1 of 4