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 regression"”.
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Essays on Semiparametric Bayesian Regression
Throughout the thesis, we emphasize that quantile regression provides a nonparametric method to construct the probabilistic model, the likelihood, so it provide a simple but powerful strategy for semiparametric Bayesian methods.
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Bayesian Regression Inference Using a Normal Mixture Model
… a two component mixture model to perform a Bayesian regression. We implement our model computationally using the Gibbs sampler algorithm and apply it to a dataset of differences in time measurement between two clocks. The dataset has ``good" time measurements and ``bad" time measurements …
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A Comparison of Bayesian Regression Models Applied in Knot Theory
This thesis explores variations on a Bayesian regression model used to estimate the mean box length of a random knot as a function of the number of edges of that knot. Specifically, this research recognizes uncertainty in box length variance and compares the resulting inference with that based on …
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New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection
… of computationally efficient procedures for regression modelling with datasets containing a large number of observations. Standard algorithms be prohibitively computationally demanding on large $n$ datasets, and we propose and analyse new computational methods for model fitting and selection. …
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The Bayesian validation metric : a framework for probabilistic model calibration and validation
… In this thesis, we propose and develop the "Bayesian Validation Metric" (BVM) as a general model validation and testing tool. We show that the BVM can represent all the standard validation metrics - square error, reliability, probability of agreement, frequentist, area, probability density …
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Robust Bayesian Anomaly Detection Methods for Large Scale Sensor Systems
… outlying sensor anomalies. We propose two Bayesian mixture model approaches that utilize heavy-tailed Cauchy assumptions. First, we propose a Robust Bayesian Regression, which utilizes a scale-mixture model to induce a Cauchy regression. Second, we extend elements of the Robust Bayesian …
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Contributions in Uncertainty Quantification Towards Reliability-based Rock Engineering Design
… discuss (ii) above in more detail. We introduce Bayesian data analysis (BDA) which allows logical augmentation of data with information from other sources (i.e. relevant historical data and expert knowledge) as a potential solution to the problem of limited data in rock engineering. Limiting our …
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A Bayesian statistics approach to updating finite element models with frequency response data
… qualified data, which is then used in a Bayesian statistics regression formulation to update the finite element model. The Bayesian formulation allows the analyst to incorporate engineering judgment (in the form of prior knowledge) into the analysis and helps ensure that reasonable and …
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Audience Reach Projection for the 2010 FIFA World Cup in South Africa
… reach. Currently we are developing a Bayesian multivariate regression with time-varying covariates. Communicating with ESPN executives and analyzing data from the 2010 NCAA March Madness Basketball championship allowed us to identify expected reach patterns for digital platforms. The …
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A tripartite study on Bayesian estimation of photosynthetically active radiation, impacts of future climate, and adaptation strategies on crop production: a spatial model framework for the Eastern Kansas River Basin
… In addition, this dissertation also developed a Bayesian regression framework to improve the estimation of PAR, which is required to enhance process-based crop model accuracy. The specific research objectives are: (1) assess the impacts of future climate conditions and root proliferation …
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Enhanced Air Transportation Modeling Techniques for Capacity Problems
… models of AROT and DROT, we fit hierarchical Bayesian regression models to the data, grouped by aircraft type using airport physical and aircraft operational parameters as the regressors. Recognizing that many existing air transportation models require distributions of AROT and DROT, Bayesian …
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DATA-DRIVEN BAYESIAN METHOD-BASED TRAFFIC CRASH DRIVER INJURY SEVERITY FORMULATION, ANALYSIS, AND INFERENCE
… from prior information and studied datasets, Bayesian models are efficient methods in data analysis with more accurate results, but their applications in traffic safety studies are still limited. By examining the driver injury severity patterns, this research is proposed to systematically …
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Understanding the Role of Social Norms in Natural Resource Co-Management
… lakes. Using randomized response techniques and Bayesian regression models, the results showed that cooperative orientations and stronger social norms are associated with greater compliance in some domains, while competitive orientations and high empirical expectations of rule-breaking predict …
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In vivo pathology markers in tauopathies: prognostic and diagnostic implications
… growth curve models (LGCMs), multiple linear regression and Bayesian regression analyses to test the prognostic value of PET and MRI, alone and in combination, to predict cognitive decline over three years. Tau burden and microglial activation in temporo-parietal cortical regions were …
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Modeling and Statistical Analysis for Low-Carbon Transition Pathways - Hydrogen Storage Optimization and Data-Driven Methane Leak Mitigation Strategies
L'abstract è presente nell'allegato / the abstract is in the attachment