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 16 of 16 for “"Bayesian Linear"”.
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Bayesian Linear Modeling in High Dimensions: Advances in Hierarchical Modeling, Inference, and Evaluation
… problems like this, the languages of linear modeling and Bayesian statistics appeal because they provide interpretability, coherent uncertainty, and the capacity for information sharing across related datasets. But at the same time, high dimensionality introduces several challenges not …
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Change point detection for high dimensional data and valid inference for Bayesian linear models
… change point detection and inference for Bayesian linear models. In the first project, we propose a change point detection method testing mean shift for high dimensional observations with unknown heteroscedasticity. The proposed tests target a dense alternative and a wild bootstrap …
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Development of a Bayesian linear regression model for the detection of a weak radiological source from gamma spectra measurements
… Previous research has been conducted on using a Bayesian model to develop a decision parameter for weak source detection. The use of a Bayesian model has been shown in laboratory settings to outperform the traditional frequentist method. However, the model tested was designed for gross counts …
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A novel spatiotemporal framework for efficient traffic prediction and visualization
… three major components, data extraction, Bayesian Linear Regression-based traffic prediction model, and an interactive map-based traffic simulator to visualize the results. To collect traffic data, we have developed an open-source web-based data scraper tool to extract and export publicly …
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Linear mixed model for multi-level omics data
… traits. In the first project, I developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient …
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A combinatorial approach to goal-oriented optimal Bayesian experimental design
… based on our goal. In this thesis, we study the Bayesian linear Gaussian model with a large number of observations, and propose several algorithms for solving the combinatorial problem of observation selection/optimal experimental design in a goal-oriented setting. Here, the quantity of interest …
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Interpolated Experience Replay for Improved Sample Efficiency of Model-Free Deep Reinforcement Learning Algorithms
… Neighborhood Mixup Experience Replay (NMER) and Bayesian Interpolated Experience Replay (BIER), modular replay buffers that interpolate transitions with their closest neighbors in normalized state-action space. NMER preserves a locally linear approximation of the transition manifold by only …
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Generating Exploration Mission-3 Trajectories to a 9:2 NRHO Using Machine Learning
… maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method …
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High-dimensional covariance estimation with applications to functional genomics
… Chapter 2 introduces a flexible and scalable Bayesian linear shrinkage covariance estimator. This accommodates multiple shrinkage target matrices, allowing the incorporation of external information from an arbitrary number of sources. It is also less sensitive to target misspecification and …
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Inferring context-specific essentiality networks using large-scale CRISPR-KO screens
… essentiality networks. I developed a Bayesian linear model called PLMCECS which identifies genes important in the context of cancer driver mutations and tissue of origin by modelling important properties of CRISPR knockout data. I validated the performance using simulated data and …
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The biomechanical consequences of body size differences in humans
… behavior in four specific areas: 1) scaling of linear anthropometric dimensions; 2) scaling of stiffness, force, displacement, and leg spring angle during running; 3) quiet standing postural sway and stance characteristics; and 4) variability and scaling patterns in bone microstructure of the …
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Building, applying, and communicating ecosystem understanding via freshwater forecasts over time and space
… at two drinking water reservoirs using a Bayesian linear model, and found process uncertainty dominated total forecast uncertainty. Additionally, I produced forecasts of water temperature and dissolved oxygen in an oligotrophic lake using a hydrodynamic-ecosystem model and found that water …
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Algorithms and Performance Analysis for Synchrophasor and Grid State Estimation
… and line parameters tolerance. Finally, a Bayesian linear state estimator (BLSE) based on a linear approximation of power flow equations for distribution networks is presented. The main advantage of BLSE is that in most cases it is so accurate as the WLS technique, but it is computationally …
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A comparative study of the effectiveness of two Bayesian models for predicting the academic successes of selected allied health students enrolled in the comprehensive community college
… models was indicated. In this context Bayesian-type models have been proposed that can utilize the strengths of both the classical statistical models and the counselor-selection models. The purpose of the study was to present and evaluate Bayesian-type models for estimating …
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Atomistic modelling of precipitation in Ni-base superalloys
… In the first part of this work we develop robust Bayesian classifiers to identify the $\gamma^{\prime}$ phase in large scale simulation boxes at high temperatures around 1500 K. We observe significant \gamma^{\prime} ordering in the simulations in the form of clusters of $\gamma^{\prime}$-like …
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Contrast preservation and constraints on individual phonetic variation
… strength. These measures are then analyzed with Bayesian linear mixed effects regression (using weakly informative priors and maximal random effects structures) in order to obtain distributional information about both populations and individual speakers. In the first experiment, word-medial …