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.
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Showing 1 to 15 of 15 for “"Variance-reduction techniques"”.
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Variance reduction techniques for estimating quantiles and value-at-risk
… extreme quantiles. This motivates applying variance-reduction techniques (VRTs) to try to obtain more efficient quantile estimators. Much of the previous work on estimating quantiles using VRTs did not provide methods for constructing asymptotically valid confidence intervals. This research …
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Variance reduction for Poisson and Markov jump processes
This thesis develops new variance reduction algorithms for the simulation and estimation of stochastic dynamic models. It provides particular application to particle dynamics models including an emissions process and radioactive decay. These algorithms apply several variance reduction techniques to …
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Combined correlation induction strategies for designed simulation experiments
This dissertation deals with variance reduction techniques (VRTs) for improving the reliability of the estimators of interest through a controlled laboratory-like simulation experiment. This research concentrates on correlation methods of VRTs which include common random numbers, antithetic …
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On-Line Monitoring, Control, and Reliability of Structural Dynamical Systems
… distribution. Several links between some MCS variance reduction techniques and Genetic Algorithms are discussed. A simple example, incorporating Genetic Algorithm operators into MCS, is shown to estimate probabilities a couple orders of magnitude smaller than standard MCS. Several concepts for …
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Bias-Free Joint Simulation of Multi-Factor Short Rate Models and Discount Factor
… Gaussian short rate model and explore the use of variance reduction techniques. We compare the exact and unbiased schemes to other solutions available in the literature: simulating the short rate under the forward measure and approximating the discount factor using quadrature.
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Efficient numerical methods for solving the Boltzmann equation for small scale flows
… limitation can be alleviated through the use of variance reduction techniques. In particular, we show that by simulating only the deviation from equilibrium, one can devise a variety of numerical methods that have a computational cost that is both small and independent of the magnitude of this …
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Performance evaluation and optimization of stochastic systems via importance sampling
… shown to depend on the performance estimator's variance. Consequently, implementing variance reduction techniques will greatly enhance the convergence properties of these algorithms. The Importance Sampling technique is employed to minimize the variance in estimating the system performance. A …
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Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning
… prompting many researchers to develop techniques which improve their scalability. This dissertation focuses on the powerful combination of iterative methods and pathwise conditioning to develop methodological contributions which facilitate the use of Gaussian processes in modern …
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Composite system reliability evaluation using sequential Monte Carlo simulation
… as benchmarks against which other approximate techniques can be compared. The focus of this research work is on the reliability evaluation of composite generation and transmission systems with special reference to frequency and duration related indices and estimated power interruption costs at …
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A simulation-based approach to assessing the relationship between mutual fund size and performance
… and investment dates are randomised and variance reduction techniques are used to improve the efficiency of the simulation, and 10 000 simulation runs are performed. The results of the simulation found a non-monotonic relationship between mutual fund size and performance over a one-year …
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Improved Sampling and Variational Inference Methods for Neural Networks
… the network's weights, giving unbiased but high variance estimates. Second, approximate inference methods that construct or optimise an approximate posterior distribution, giving biased estimates, usually with low variance. Variational Inference (VI) is a commonly used method in this class. This …
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Uncertainty-Based Design Optimization and Decision Options for Responsive Maneuvering of Reconfigurable Satellite Constellations
… of simulation cases that may be modelled, so variance reduction techniques are applied to lower the standard error of mean performance in the output, allowing for a reduction in optimization size and runtime while maintaining the same level of error in the predicted results. Decision options …
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Development of a Novel Detector Response Formulation and Algorithm in RAPID and its Benchmarking
… These methods can be categorized by: (1) Using variance reduction techniques to improve successive rate of Monte Carlo method; and (2) Developing numerical techniques to improve convergence rate and avoid unphysical behavior for deterministic method. These methods are considered clever and …
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Stochastic simulation of power systems with integrated renewable and utility-scale storage resources
… approach makes use of Monte Carlo simulation techniques to represent the impacts of the sources of uncertainty on the side-by-side power system and market operations. As such, we systematically sample the ``input'' random processes – namely the buyer demands, renewable resource outputs and …
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Hardware acceleration of Monte Carlo-based simulations
… performance of FPGAs. • Methods, algorithms and techniques suited for FPGAs. • Design tools. • Hardware-Software co-design and integration. Studying in depth each one of these five challenges related to hardware acceleration is not feasible in just one Thesis. The great variety of applications …