Essays about: "Deterministic sampling"
Showing result 1 - 5 of 8 essays containing the words Deterministic sampling.
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1. Inverse Uncertainty Quantification using deterministic sampling : An intercomparison between different IUQ methods
University essay from Uppsala universitet/Tillämpad kärnfysikAbstract : In this thesis, two novel methods for Inverse Uncertainty Quantification are benchmarked against the more established methods of Monte Carlo sampling of output parameters(MC) and Maximum Likelihood Estimation (MLE). Inverse Uncertainty Quantification (IUQ) is the process of how to best estimate the values of the input parameters in a simulation, and the uncertainty of said estimation, given a measurement of the output parameters. READ MORE
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2. Squaring VEGAS -- Multidimensional Integration Using 2D Correlations
University essay from Lunds universitet/Teoretisk partikelfysik - Geonomgår omorganisationAbstract : Multidimensional integrals are used in many areas of physics; electrodynamics, quantum mechanics and statistical physics, to name a few. Many integrals can not be solved analytically, but can instead be approximated numerically. VEGAS is a Monte Carlo integration algorithm, which specializes it in approximating multidimensional integrals. READ MORE
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3. Convolutional neural network based object detection in a fish ladder : Positional and class imbalance problems using YOLOv3
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Hydropower plants create blockages in fish migration routes. Fish ladders can serve as alternative routes but are complex to install and follow up to help adapt and develop them further. In this study, computer vision tools are considered in this regard. READ MORE
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4. An Empirical Evaluation of Context Aware Clustering of Bandits using Thompson Sampling
University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)Abstract : Stochastic bandit algorithms are increasingly being used in the domain of recommender systems, when the environment is very dynamic and the items to recommend are frequently changing over time. While traditional approaches consider a single bandit instance which assumes all users to be equal, recent developments in the literature showed that the quality of recommendations can be improved when individual bandit instances for different users are considered and clustering techniques are used. READ MORE
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5. Ensemble for Deterministic Sampling with positive weights : Uncertainty quantification with deterministically chosen samples
University essay from Uppsala universitet/Tillämpad kärnfysikAbstract : Knowing the uncertainty of a calculated result is always important, but especially so when performing calculations for safety analysis. A traditional way of propagating the uncertainty of input parameters is Monte Carlo (MC) methods. A quicker alternative to MC, especially useful when computations are heavy, is Deterministic Sampling (DS). READ MORE
