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 20 of 23 for “"predictive uncertainty"”.
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Optimization Under Uncertainty and Total Predictive Uncertainty for a Tractor-Trailer Base-Drag Reduction Device
… 3-D aerodynamic shape optimization under uncertainty (OUU) study. To gain confidence in the accuracy and precision of a computational fluid dynamics (CFD) flow solver and its Reynolds-averaged Navier-Stokes (RANS) turbulence models, it is necessary to conduct code verification, solution …
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Construction of predictive uncertainty quantification framework to the extension of TPS plasma wind tunnel experiment data to flight conditions
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Monitoring and modelling hydrological response and sediment yield in a North York Moors catchment : an assessment of predictive uncertainty in a coupled hydrological-sediment yield model
… was developed. An assessment was made of the predictive uncertainty in the individual model predictions, as well as the uncertainty propagated from the primary hydrological model to the secondary sediment yield model, using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. …
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Efficient, robust and uncertainty aware mobile health
… deep learning models cannot capture the predictive uncertainty leading to overconfident predictions, and, ultimately, they are less robust in real-world applications. Bayesian deep learning or other non-Bayesian probabilistic techniques can naturally quantify such uncertainty. Still, they …
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Trustworthy Soft Sensing in Water Supply Systems using Deep Learning
… the robustness of soft sensors by estimating predictive uncertainty and evaluating performance across various scenarios. The framework facilitates comparisons between hard and soft sensors. To validate the framework, I conduct experiments using data generated by AI and Cyber for Water and Ag …
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Statistical Evaluation of Deep Learning for Event Detection in Time Series: Quantifying Uncertainty, Efficiency, and Adaptation with Applications to Seismic Data
… which ignores important questions about how predictive performance varies with data availability, how uncertainty is communicated in both predictions and aggregate metrics, and how shifting data distributions impact model reliability. Presented as three studies, this dissertation develops …
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Numerical Validation and Refinement of Empirical Rock Mass Modulus Estimation
… compared to the predicted relationship and predictive uncertainty at low GSI values. In this research, a practical range of rock mass quality, as defined by GSI, including "Blocky\Disturbed\Seamy" rock masses, "Very Blocky" and relatively competent rock masses are analyzed using discretely …
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Learning-based crop management optimization using multi-stream convolutional neural networks
… regression problem to better predict yield. The predictive model is then used as the base of a gradient-ascent algorithm to maximize a custom objective function. To leverage the applicability of this algorithm, a risk-aware version of this method is also proposed. The predictive uncertainty is …
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Modeling the exchange of energy and matter within and above a spruce forest with the higher-order closure model ACASA
… both the exploration of the sensitivity and predictive uncertainty of the modeled fluxes and the analysis and correction of model errors that were encountered while working with the model. Furthermore, the ability of the ACASA model to reproduce measured quantities within and above the forest …
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Uncertainty Assessment in High-Risk Environments Using Probability, Evidence Theory and Expert Judgment Elicitation
<p>The level of uncertainty in advanced system design is assessed by comparing the results of expert judgment elicitation to probability and evidence theory. This research shows how one type of monotone measure, namely Dempster-Shafer Theory of Evidence can expand the framework of uncertainty to …
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Probabilistic Time-to-Event Modeling Approaches for Risk Profiling
… models that account for calibration and uncertainty, while predicting accurate absolute event times. Specifically, we introduce an adversarial nonparametric model for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We consider …
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Methods to Improve Fairness and Accuracy in Machine Learning, with Applications to Financial Algorithms
… to algorithmic decision-making. I show that predictive uncertainty often leads algorithms to systematically disadvantage groups with lower-mean outcomes, assigning them smaller true and false positive rates than their higher-mean counterparts. I prove that this disparate impact can occur even …
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Estimating Uncertainty in HSPF based Water Quality Model: Application of Monte-Carlo Based Techniques
To propose a methodology for the uncertainty estimation in water quality modeling as related to TMDL development, four Monte Carlo (MC) based techniques—single-phase MC, two-phase MC, Generalized Likelihood Uncertainty Estimation (GLUE), and Markov Chain Monte Carlo (MCMC) —were applied to a …
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Causal Inference and Evidence-Grounded Language Models for Trustworthy Personalized Clinical Decision Support
… support systems (CDSS) are evolving from passive predictive tools into active collaborators in clinical reasoning. However, most machine learning approaches remain limited to risk prediction, lacking the causal reasoning, patient-specific personalization, and evidence-verifiable justification …
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Advances in Probabilistic Deep Learning and Their Applications
… models must be able to quantify their uncertainty. To this end, we develop a new probabilistic deep learning method that performs Bayesian inference over just a subset of a neural network’s parameters. We propose a way to choose such subnetworks to faithfully preserve the model‘s …
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Machine Learning for Optical Fibre Communication Systems
… interpretable model outputs, well-quantified predictive uncertainty and transparent model design, and discuss to what extent these properties are satisfied by the work in this thesis. First, we focus on estimation of the physical layer parameters at the receiver, to increase the capacity by …
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Robust Inference via Optimal Transport Ambiguity Sets
Uncertainty quantification is pivotal for ensuring the safety and reliability of predictive algorithms in high-stakes applications—ranging from cancer diagnosis to autonomous driving. This challenge is exacerbated by distribution shift, in which the true data–generating distribution diverges from …
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ROBOT NAVIGATION IN CROWDED DYNAMIC SCENES
… Furthermore, this dissertation proposes a novel predictive uncertainty-aware navigation framework to improve the safety performance of current existing control policies by incorporating the output of the proposed stochastic environment prediction algorithms into general navigation frameworks. …
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Optimizing wastewater treatment sampling strategies through Markov Chain Monte Carlo Bayesian inference in Activated Sludge Model No. 3
… by challenges in parameter estimation and uncertainty quantification under realistic monitoring conditions. This thesis develops a comprehensive Bayesian inference framework for Activated Sludge Model No. 3 that integrates sensitivity screening, identifiability diagnostics, and Markov chain …
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On the effects of hydrological uncertainty in assessing the impacts of climate change on water resources
… climate change have to be seen as a "cascade of uncertainty", in which decisions taken in every step of the assessment process convey uncertainties that are unavoidably propagated to subsequent levels. At the other hand, uncertainties in projections of climate models and those involved in the …
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