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 199 for “"Model uncertainty"”.
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Model Uncertainty & Model Averaging Techniques
… research is to shed more light on the issue of model uncertainty in applied econometrics in general and cross-country growth as well as happiness and well-being regressions in particular. Model uncertainty consists of three main types: theory uncertainty, focusing on which principal determinants …
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Statistical Hypothesis Testing Under Model Uncertainty
Statistical testing is one of the main problems in statistics and finds applications in a number of fields, including engineering, signal processing, medicine, and finance among others. Traditionally in hypothesis testing problem, the hypothesis distributions subject to testing are known. However, …
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Topics on statistical inference with model uncertainty
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Bayesian Two Stage Design Under Model Uncertainty
… used to efficiently generate data for an assumed model y = f(x<sup>(m)</sup>,b) + ε. The model assumptions include the form of f, the set of regressors, x<sup>(m)</sup> , and the distribution of ε. The nature of the response, y, often provides information about the model form (f) and the error …
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Essays on Online Decisions, Model Uncertainty and Learning
… of the following components: online decisions, model uncertainty and learning. The first model studies the problem of online selection of a monotone subsequence and provides distributional properties of the optimal objective function. The second model studies the robust optimization approach to …
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Including model uncertainty in risk-informed decision-making
Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic …
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Robust decision-making with model uncertainty in aerospace systems
… can be extremely sensitive to errors in the models, and this research addressed the role of robustness in coping with this uncertainty. The first part of this thesis presents a computationally efficient sampling methodology, Dirichlet Sigma Points, for solving robust Markov Decision Processes …
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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 …
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Measuring Machine Learning Model Uncertainty with Applications to Aerial Segmentation
<p>Machine learning model performance on both validation data and new data can 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 …
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Model uncertainty and performance analysis for precision controlled space structures
… our goal is to predict the amount of uncertainty in the performance prediction made through out the design process. Also, given a statistical database for structural uncertainty, the methodology presented will establish the probability of success of a particular architecture. The …
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Compensating for model uncertainty in the control of cooperative field robots
… to uncertainties in the environment, task, robot models and sensors. A key problem is that it is often difficult to directly measure key information required for the control of interacting cooperative mobile robots. The objective of this research is to develop algorithms that can compensate for …
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Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
<p>The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prior distributions in an objective manner, providing conditions under which …
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Progressive learning of endpoint feedback systems with model uncertainty and sensor noise
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.
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An Iterative LQR Method for Addressing Model Uncertainty in the Mars Entry Problem
… capable of incorporating the atmospheric models and navigational data uncertainty for the nonlinear dynamics of hypersonic entry by applying an iterative Linear-Quadradic-Regulator (iLQR). iLQR is an efficient and powerful method for trajectory optimization derived from Differential …
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Computational fluid dynamics and turbulence model uncertainty quantification for nuclear reactor safety applications
… (RANS) equations describing fluid flow. Uncertainty arises in CFD simulations due to a variety of sources, and this uncertainty must be rigorously quantified in order to be useful in support of reactor licensing and decision making. In traditional system thermal hydraulics codes, the code …
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Examination of model uncertainty and parameter sensitivity in correlated systems using covariance structure analysis
Correlated parameters are often expected when modeling a natural system. However, correlation among the variables often blurs the model uncertainty and makes it difficult to determine parameter sensitivity. In simple systems, model structure and uncertainty can be explained directly; however, …
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Structure incorporation of model uncertainty for Bayesian adaptive tracking and its application to maritime surveillance
… entire task, including the adaptive observation model, within the Bayesian inference. In this thesis we develop a framework for simultaneous modelling and estimation (SMAE), in which the common Bayesian recursive estimator (BRE) is extended to include estimation of the underlying hidden Markov …
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An Analysis of Hydrological Model Uncertainty at the Local Stage of a Climate Change Impact Assessment in the Suir Catchment
This thesis presents an analysis of uncertainty at the local stage of a climate impact assessment. Impact model structural uncertainty and uncertainty due to equifinality of parameter sets are evaluated, in addition to uncertainty due to GCMs and emissions scenarios. The Suir catchment is employed …
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