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 54 for “"variance-reduction"”.
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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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Efficient reinforcement learning through variance reduction and trajectory synthesis
… learning algorithms suffer from large variance and sampling inefficiency, which leads to slow convergent rate as well as unstable performance. In this thesis, we manage to alleviate these two relevant problems. For enormous variance, we combine variance reduced optimization with deep …
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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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Weighted particle variance reduction of Direct Simulation Monte Carlo for the Bhatnagar-Gross-Krook collision operator
… a control-variate-based approach to obtain a variance-reduced DSMC method that dramatically enhances statistical convergence for lowsignal problems. Here we focus on the Bhatnagar-Gross-Krook (BGK) approximation, which as we show, exhibits special stability properties. The BGK collision …
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Monte Carlo modeling and variance reduction technique applications for heavily shielded geometries in the decommissioning of nuclear power plants
L'abstract è presente nell'allegato / the abstract is in the attachment
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Importance Resampling for Global Illumination
… Importance Sampling can lead to significant variance reduction over standard Monte Carlo integration for common rendering problems. We show how to select the importance resampling parameters for near optimal variance reduction. We also combine RIS with stratification and with Multiple …
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An excursion with the Boltzmann equation at low speeds : variance-reduced DSMC
… becomes overwhelming. This thesis presents a variance reduction approach for reducing the statistical uncertainty associated with low-signal flows thus making their simulation not only possible but also efficient. Variance reduction is achieved using a control variate approach based on the …
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A Study in Hybrid Monte Carlo Methods in Computing Derivative Prices
… These methods can be considered as ways of variance reduction. The thesis also introduces a new variance reduction method using orthogonal transformation which further reduces the variance. It is shown in this thesis that the HMC methods can significantly improve the efficiency when compared …
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Development of a heuristic methodology for designing measurement networks for precise metal accounting
… well as from an intensive numerical study on the variance reduction response of measurements after data reconciliation conducted in this study. These were referred to as 'mathematical heuristics' and are based on the general principle of variance reduction through data reconciliation. It was …
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Kernel method in Monte Carlo importance sampling
… of structural systems using a Monte Carlo variance reduction technique called the Importance Sampling is presented. Since the efficiency of the importance sampling method depends primarily on the choice of the importance sampling density, the use of the kernel method to estimate the optimal …
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Learning-accelerated algorithms for simulation and optimization
… two algorithms are developed: (1) a variance reduction algorithm for Monte Carlo simulations of mean-field particle systems, and (2) a global optimization algorithm for noisy expensive functions. For the variance reduction algorithm, we develop an adaptive-control-variates technique …
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Computational modeling of biological molecule separation in nanofluidic devices
… In the second part of this Thesis we present a variance reduction methodology for reducing the statistical uncertainty of Brownian Dynamics simulations. Our formulation is based on the recent method of Al-Mohssen and Hadjiconstantinou which uses importance weights within a control variate …
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Discontinuous Galerkin solution of the Boltzmann equation in multiple spatial dimensions
… and N. G. Hadjiconstantinou recently developed a variance reduction technique [5] in which one only simulates the deviation from equilibrium. This thesis presents the implementation of this variance reduction approach to a Runge-Kutta Discontinuous Galerkin finite element formulation in multiple …
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Exploration of parameters for the continuous blending of pharmaceutical powders
… effect of raw and intermediate variables on the variance reduction ratio. Significant parameters identified included the choice of API, fill fraction, the number of blade passes, the mean residence time, the Bodenstein number, and the period of input feed fluctuations. The results highlight the …
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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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Empirical Evaluation of the Risk and Cost Effects of Geographic Diversification in Central Illinois Grain Farms
… tract to tract, far outweigh the derived risk (variance) reduction benefits. Survey results also showed that farmers appear to be aware of this unfavorable tradeoff. The results of the simulation also indicate that while geographic diversification may not be an attractive risk management …
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Functional quantization-based stratified sampling
… stratified sampling is a method for variance reduction proposed by Corlay and Pagès (2015). This method requires the ability to both create functional quantizers and to sample Brownian paths from the strata defined by the quantizers. We show that product quantizers are a suitable …
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Pricing swaptions on amortising swaps
… the convergence rate of the Monte Carlo model, a variance reduction method, namely the control variates technique and a method of using deterministic low-discrepancy sequences (also called quasi-Monte Carlo methods).
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DOES PAIR-MATCHING ON ORDERED BASELINE MEASURES INCREASE POWER: A SIMULATION STUDY
… with normally distributed measures reduces the variance of the estimated treatment effect (Park and Johnson, 2006). The main objective of this study is to examine if pair-matching improves the power when the distribution is a mixture of two normal distributions. Multiple scenarios with a …
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