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 26 for “"hierarchical Bayesian model"”.
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Bayesian Inference of the Weibull-Pareto Distribution
… has is its shape can skew being able to better model left or right skewed data. Examples of skewed data include human longevity and actuarial data. In this work a hierarchical Bayesian model was developed using the Weibull-Pareto distribution.</p>
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Geography: Its Place in Higher Education Enrollment
… methods. In addition, the incorporation of a Hierarchical Bayesian model will effectively model influential enrollment factors, which successful students possess. Hierarchical Bayesian models use the prior distribution, and likelihood of an events occurrence to create the posterior …
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High compression rate text summarization
… summarization methods is due to their in-depth modelling of document content in a probabilistic framework. We explore two types of document representation that capture orthogonal aspects of text content. The first represents the semantic properties mentioned in a document in a hierarchical …
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Applications and Computation of Stateful Polya Trees
… priors on distributions which are able to model absolutely continuous distributions directly, rather than modeling a discrete distribution over parameters of a mixing kernel to obtain an absolutely continuous distribution. The Polya tree discretizes the state space with a recursive …
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A bayesian approach to wireless location problems
… wireless networks are proposed. We explore non-hierarchical and hierarchical Bayesian graphical models that use prior knowledge about physics of signal propagation, as well as different modifications of Bayesian bivariate spline models. The hierarchical Bayesian model that incorporates …
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Risk Management in Air Traffic Applications: Data-Driven Modeling, Prediction, and Generation of Realistic Weather Disruptions and Other Unfavorable Conditions
… which can be important both in building models and using them to make predictions, and generate test cases to stress-test proposed design decisions. In this thesis, we develop a hierarchical Bayesian model for air traffic network operations, and investigate methods for learning these …
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The effect of locational uncertainty in geostatistics
… drawn from the data;We propose a statistical model for incorporating locational error into spatial data analysis. We investigate the effect of locational error on the spatial lag, the covariance function, the variogram, and optimal spatial prediction (aka, kriging). We show that the basic …
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Parallel and Nonparallel Patterns of Genetic Co-Differentiation: Evidence for Host Associated Differentiation Among Trophic Levels of the Oak Gall Wasp System
… using Principal Component Analysis and the hierarchical Bayesian model ENTROPY to assign individuals to genetic clusters and estimate admixture proportions. ENTROPY revealed significant substructure within Synergus sp. corresponding to five independent lineages, three of which represent …
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Bayesian Saltwater Intrusion Prediction and Remediation Design under Uncertainty
… resources. However, the groundwater simulation models are subjected to uncertainty in their predictions. The goals of this research are to: (1) quantify the uncertainty in the groundwater model predictions and (2) investigate the impact of the quantified uncertainty on the aquifer remediation …
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Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
… in data. In traditional nonparametric mixture models, observations are usually assumed exchangeable, even though dependence often exists associated with the space or time at which data are generated.</p> <p>Focused on model-based clustering and segmentation, this thesis addresses the issue in …
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In search of functional specificity in the brain : generative models for group fMRI data
… areas. Our analysis method relies on generative models that explain fMRI data across the group as collections of brain locations with similar profiles of functional specificity. We refer to each such collection as a functional system and model it as a component of a mixture model for the data. …
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Scaling in the immune system
… immune search and response. We fit mathematical models to experimental West Nile Virus (WNV, a multi-host pathogen) infection data and investigate how model parameters characterizing the pathogen and the immune response change with respect to animal mass. Phylogeny also affects pathogenesis and …
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Unraveling Complexity: Panoptic Segmentation in Cellular and Space Imagery
… deep learning, have facilitated the creation of models capable of performing tasks previously thought impossible. This progress has opened new possibilities across diverse fields such as medical imaging and remote sensing. However, the performance of these models relies heavily on the …
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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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Contributions in Uncertainty Quantification Towards Reliability-based Rock Engineering Design
… of limit state functions stemming from model uncertainty, and (ii) parameter uncertainties stemming from lack of sufficient quantitative data. With regards to (i) above, this thesis demonstrates the existence of a significant component of neglected and unquantifiable model uncertainty in …
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The Andromeda Optical and Infrared Disk Survey
… image-to-image surface brightness, and a novel hierarchical Bayesian model to trace the background signal while modelling the astrophysical SED. We model the spectral energy distributions of M31 pixels with MAGPHYS (da Cunha et al. 2008) and compare those results to resolved stellar population …
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A bayesian hierarchical modelling of small area variation in youth unemployment in Namibia
… to conventional small area estimation (SAE) models, the hierarchical Bayesian approach to SAE problems has several benefits, one of which is the ability to properly account for the kind of surveyed variable. For this reason, the main objective of this study was to estimate the risk of youth …
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Fast Stars in the Milky Way
… establish. In Ch. 2, I develop a sophisticated Bayesian methodology to search the nearest ten remnants for a companion, by combining data from Gaia DR1 with a 3D dust-map and binary population synthesis. With Gaia DR2, I will identify companions of tens of supernova remnants and thus open a new …
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Evolution and ecology of C4 grasses: insights from carbon isotope analysis of grass pollen grains
… expanding its training dataset and developing a hierarchical Bayesian model that increases precision of C4 grass estimates. I applied the refined technique to samples from the Oligocene and Miocene in Western Europe, a region of the world with no previous documented evidence of C4 grasses, yet …
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Large Scale Machine Learning in Biology
… informative patterns and clinically predictive models using this data. Motivated by an existing graph partitioning framework, we first derive relationships between optimizing the regularized min-cut cost function used in spectral clustering and the relevance information as defined in the …
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