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 185 for “"Bayesian Framework"”.
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A Bayesian framework for concept learning
… This thesis proposes a new computational framework for understanding how people learn concepts from examples, based on the principles of Bayesian inference. By imposing the constraints of a probabilistic model of the learning situation, the Bayesian learner can draw out much more …
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Modeling Arthropod Traits in a Bayesian Framework
… The purpose of this work is to use Bayesian methods to fit the most accurate TPCs with the least uncertainty based on data availability, as well as investigate certain overlooked pieces. Specifically, we conduct simulation experiments to explore the effect of the data-generating …
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Combining Prior Knowledge and Data: Beyond the Bayesian Framework
… For reinforcement learning, we introduce a novel framework for defining and solving planning problems in terms of qualitative statements about the world. In compiler optimization, Bayesian prior based on an analytic model of hardware is combined with empirical measurements of performance of …
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Approximate credibility intervals on electromyographic decomposition algorithms within a Bayesian framework
This thesis develops a framework to uncover the probability of correctness of algorithmic results. Specifically, this thesis is not concerned with the correctness of these algorithms, but with the uncertainty of their results arising from existing uncertainty in their inputs. This is achieved using …
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A Bayesian Framework for Multi-Stage Robot, Map and Target Localization
This thesis presents a generalized Bayesian framework for a mobile robot to localize itself and a target, while building a map of the environment. The proposed technique builds upon the Bayesian Simultaneous Robot Localization and Mapping (SLAM) method, to allow the robot to localize itself and the …
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Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
… We formulate the inverse UQ process under the Bayesian framework using the ``model updating equation''. Markov Chain Monte Carlo (MCMC) sampling is applied to explore the posterior distributions and generate samples from which we can extract statistical information for the uncertain input …
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A Bayesian framework for statistical signal processing and knowledge discovery in proteomic engineering
… this information. This work seeks to develop a Bayesian framework in mass-based proteomics for protein identification. Using the Bayesian framework in a statistical signal processing manner, mass spectrometry data is filtered and analyzed in order to estimate protein identity. This is done by a …
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A Bayesian Framework for the Unified Model for Assessing Cognitive Abilities: Blending Theory With Practicality
… & Roussos (1995). At the end of Chapter 1, a Bayesian framework is given to the model in preparation for the discussion in Chapter 2 of the Markov Chain Monte Carlo algorithm used to estimate the RUM model parameters. Then, the estimation accuracy of the algorithm and the robustness of the …
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Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation-maximization under non-Bayesian and Bayesian framework
… model parameter uncertainty. A mathematical framework is developed where Expectation-Maximization (EM) algorithm is implemented to quantify input model parameter uncertainty using the Maximum Likelihood Estimate (MLE) and Maximum a Posteriori (MAP) estimate. The difference between …
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Applications of the Bayesian approach for experimentation and estimation
A Bayesian framework for systematic data collection and parameter estimation is proposed to aid experimentalists in effectively generating and interpreting data. The four stages of the Bayesian framework are: system description, system analysis, experimentation, and estimation. System description …
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Bayesian artificial neural networks in water resources engineering.
A new Bayesian framework for training and selecting the complexity of artificial neural networks (ANNs) is developed in this thesis, based on Markov chain Monte Carlo (MCMC) techniques. The primary motivation of the research presented is the incorporation of uncertainty into ANNs used for water …
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Time series modelling and inference with Bayesian Context Trees
… ideas and algorithmic tools to build a general Bayesian framework for modelling and inference with time series data, which is found to be effective in a number of practical settings, including both discrete and real-valued observations. For discrete-valued time series, we describe a novel …
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Graphical tools for the examination of high-dimensional functions obtained as the result of Bayesian analysis
Bayesian statistics has a tendency to produce objects that are of many more than three dimensions, typically of the same dimensionality as the parameter set of the problem. This thesis takes the idea of visual, exploratory data analysis and attempts to apply it to those objects. In order to do this …
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Speech enhancement using deep dilated CNN
… limitations regarding existing works. First, the Bayesian framework is not adopted in many such deep-learning-based algorithms. In particular, the prior distribution for speech in the Bayesian framework has been shown useful by regularizing the output to be in the speech space, and thus improving …
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Building Black Holes: Analogue experiments and analogical reasoning
… analogue experimentation. Chapter 2 expands on a Bayesian framework introduced by Dardashti et al. (2019) to argue that analogue experiments can in principle provide significant confirmation for claims about their target systems, but only when supplemented with an independently plausible claim …
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Bug vision : experiments in low resolution vision
… well as point tracking strategies are discussed. Bayesian solutions to the point-tracking problem are well understood, because the generative models need describe the dynamics of simple point objects. In addition, the radar tracking problem assumes that measurements are noise corrupted positions, …
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Examining agricultural investment
… regression approach is conducted under a Bayesian framework with variable selection and outlier detection components. The results imply strong support for the accelerator model of investment and the inclusion of other relevant variables, among them the value of short-term assets, one of …
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Fast algorithms for Bayesian variable selection
… on penalized likelihood, and the other based on Bayesian framework. We focus on the Bayesian framework in which a hierarchical prior is imposed on all unknown parameters including the unknown variable set. The Bayesian approach has many advantages, for example, we can access unknown obtain the …
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Bayesian analysis of historical functional linear models with application to air pollution forecasting
… the current outcome. In this work, we develop a Bayesian framework for the analysis of the historical functional linear model with multiple predictors. Different from existing Bayesian approaches to historical functional linear models, our proposed methodology is able to handle multiple …
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Bayesian Modeling for Isoform Identification and Phenotype-specific Transcript Assembly
… dissertation research, we have developed novel Bayesian approaches to infer alternative splicing mechanisms in biological systems using RNA sequencing data. Specifically, we focus on two research topics in this dissertation: isoform identification and phenotype-specific transcript assembly. For …
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