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 46 for “"Epistemic Uncertainty"”.
-
Generative modelling under epistemic uncertainty
… an inability to distinguish between aleatoric uncertainty (data ambiguity) and epistemic uncertainty (lack of knowledge). This thesis addresses these limitations by establishing a rigorous framework for Random-Set Deep Learning, shifting from point-estimate probabilities to belief functions …
-
Epistemic Uncertainty Quantification in Scientific Models
<p>In the field of uncertainty quantification (UQ), epistemic uncertainty often refers to the kind of uncertainty whose complete probabilistic description is not available, largely due to our lack of knowledge about the uncertainty. Quantification of the impacts of epistemic uncertainty is …
-
Asteroid deflection campaign design integrating epistemic uncertainty
… extreme care for decision makers due to inherent uncertainty. The forms of uncertainty can be epistemic or aleatoric. Epistemic uncertainty can be reduced by replenishing incomplete information with better observations, whereas stochastic uncertainty cannot be reduced owing to its randomness. …
-
Decision making under epistemic uncertainty : an application to seismic design
The problem of accounting for epistemic uncertainty in risk management decisions is conceptually straightforward, but is riddled with practical difficulties. Simple approximations are often used whereby future variations in epistemic uncertainty are ignored or worst-case scenarios are postulated. …
-
Characterizing the Epistemic Uncertainty of Predictive Action Models and Sampling-Based Motion Planners for Robotic Manipulation
We derive methods to represent the epistemic uncertainty of models used in long-horizon robot planning problems in autonomous manipulation. We develop a representation of epistemic uncertainty for two types of models: uncertainty over the physical parameters of a model that predicts the observed …
-
Advancing Efficiency and Safety in Autonomous Sequential Decision Making
… environments, addressing inaccuracies under uncertainty, and implementing effective risk management strategies. It focuses on boosting RL data efficiency through simultaneous emphasis on epistemic uncertainty and knowledge transfer, and on mitigating risks associated with agent actions …
-
Uncertainty Estimation for Single Stage Object Detection
… applications, necessitating robust methods for uncertainty quantification. Although full Bayesian inference would provide the most principled treatment of uncertainty, it is computationally impractical or even infeasiblefor modern largescale models and real-time detection pipelines. However, the …
-
Learning-Based Complex Terrain Navigation Under Uncertainty
… properly quantifying and mitigating risk due to uncertainty in learned models and improving model generalization in novel environments. To address these challenges, this thesis presents a unified framework to learn uncertainty-aware, physics-informed traversability models and achieve risk-aware …
-
Computational Reconstruction and Quantification of Aerospace Materials
… second focus is on the analysis of the numerical uncertainty (epistemic uncertainty) that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty (aleatoric uncertainty), which is the noise that is inherent in the original image representing the …
-
Who Should I Trust? Uncertainty and Risk for Knowledge Transfer from Multiple Sources in Reinforcement Learning Domains
… remain largely incognizant to risk and uncertainty during transfer. In this thesis, we identify two sources of risk that must be addressed in order to make transfer learning from multiple knowledge sources more reliable and autonomous: epistemic uncertainty arises due to a lack of …
-
A reliability rating system for flood embankments in the United Kingdom
… defence network, a diffuse lack of knowledge (epistemic uncertainty) about some of the characteristics of the structures has to be addressed. This thesis presents a new methodology, named the Reliability Rating System, which makes possible the rapid quantification of the expected performance of …
-
Indices of Social Vulnerability to Hazards: Model Uncertainty and Sensitivity
… observable. This research applies global uncertainty and sensitivity analyses to internally validate the methods used in the most common social vulnerability index designs. Global uncertainty analysis is performed to assess the robustness of index ranks when reasonable alternative index …
-
Measuring Machine Learning Model Uncertainty with Applications to Aerial Segmentation
… be better measured and understood by leveraging uncertainty metrics at the time of prediction. These metrics can improve the model training process by indicating which training data need to be corrected and what part of the domain needs further annotation. The methods described have yet to reach …
-
Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
… supporting tasks such as design optimization, uncertainty analysis, and autonomous experimentation. However, scientific surrogates are often deployed in data-scarce and extrapolative regimes, where predictive accuracy alone is insufficient. Reliable uncertainty quantification and appropriate …
-
Epistemic deep learning : enabling machine learning models to ‘know when they do not know’
… hindered by an inherent inability to manage uncertainty, resulting in overconfident and unreliable predictions when models encounter out-of-distribution data, adversarial perturbations, or naturally fluctuating environments. This thesis, titled Epistemic Deep Learning: Enabling Machine …
-
Towards Managing and Understanding the Risk of Underwater Terrorism
… to produce distributions and do not integrate epistemic uncertainty. Other methods rely on locating subject matter experts who can provide judgment and then undertaking an associated validation of these judgments.</p> <p>Using experimentation, data from unclassified successful, or near …
-
Decision Analysis in Fire Safety Engineering - Analysing Investments in Fire Safety
… above all at the handling of cases of large epistemic uncertainty regarding both probabilities and utilities, Bayesian decision theory serving as a basis for the development of the method. Two extensions of the decision rule used in Bayesian decision theory (the principle of maximising …
-
Local polynomial chaos expansion method for high dimensional stochastic differential equations
… widely adopted method to determine evolution of uncertainty in dynamical system with probabilistic uncertainties in parameters. In particular, we focus on linear stochastic problems with high dimensional random inputs. Most of the existing methods enjoyed the efficiency brought by PC expansion …
-
Rethinking Uncertainty: Spinoza and Hume on Shaping Uncertain Secular Futures
… dissertation extends contemporary views about uncertainty. It does so through a reading of the role of uncertainty in the political thought of two modern philosophers, Baruch Spinoza and David Hume. Despite uncertainty's notable and multi-disciplinary appeal in the academic literature, the …
-
Multi-Level Quantization of Stochastic Variational Inference based Bayesian Neural Networks
… power of standard neural networks with the uncertainty estimation capabilities of Bayesian Inference, offering a robust framework to address challenges such as overconfidence and overfitting. However, the inherent complexity of BNNs —due to the use of weight distributions— renders the …
Page 1 of 3