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 105 for “"Hierarchical Bayesian"”.
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Hierarchical Bayesian Dataset Selection
… first dataset selection algorithm that utilizes hierarchical Bayesian modeling, designed for collaborative data-sharing ecosystems. The proposed method efficiently decomposes the contributions of dataset groups and individual datasets to local model performance using Bayesian updates with small …
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Hierarchical Bayesian Models for Multimodal Neuroimaging Data
… the clinical outcome of interest. Furthermore, Bayesian priors are used to inform the selection of imaging markers with external imaging data. We assess the performance of our method on synthetic data and compare its performance to competing methods. We demonstrate use of the proposed method for …
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Learning motion patterns using hierarchical Bayesian models
… from complicated and large scale data sets using hierarchical Bayesian models. We explore their applications to activity analysis in far-field visual surveillance and tractography segmentation in medical imaging. Many existing activity analysis approaches in visual surveillance are ad hoc, relying …
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Hierarchical Bayesian Analysis of Peruvian Tree Growth Rates
This thesis explores the use of Bayesian statistical methods and hierarchical modeling in order to analyze massive data sets of Peruvian tree growth data. The study is important in finding connections between different parameters (such as the tree's classification or elevation) and rate at which …
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Hierarchical Bayesian approaches to seismic imaging and other geophysical inverse problems
… the inferred model parameters. In the context of Bayesian inference, these smoothness assumptions take the form of a prior distribution on the model parameters. Conventionally, the regularization parameters defining these assumptions are fixed independently from the data or tuned in an ad hoc …
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Hierarchical Bayesian Models for Investigating Astrophysical Systematics in Type Ia Supernova Cosmology
… thesis, and discuss statistical methods such as hierarchical Bayesian modelling and Gaussian processes in Chapter 2, which are used extensively throughout. In the subsequent Chapters, I explore two distinct avenues for better understanding empirical SN-host correlations. The first involves the …
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Dual Mechanisms of Cognitive Control: A Hierarchical Bayesian Approach to Test-Retest Reliability
… (e.g., split-half, ICC, and rho), and the second hierarchical Bayesian, we provide evidence that (1) reliable individual differences can be extracted from experimental tasks, and (2) weak correlations between tasks of cognitive control are not solely caused by the attenuation of unreliable …
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A hierarchical Bayesian calibration framework for quantifying input uncertainties in thermal-hydraulics simulation models
… ``lack of input uncertainty information'' issue. Bayesian calibration, or inverse Uncertainty Quantification (UQ), is the process of updating uncertainty distributions on the model inputs in a way that is consistent with observed data. The process of Bayesian calibration for nuclear system codes …
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Semantically aware hierarchical Bayesian network model for knowledge discovery in data : an ontology-based framework
… and flexible framework that integrates the Hierarchical Bayesian Network (HBN) and domain ontology. The ultimate aim of this thesis is to propose a data mining framework that implicitly caters for the underpinning domain knowledge and eventually leads to a more intelligent and accurate …
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A control charts methodology to multistage process monitoring and root cause diagnosis using hierarchical Bayesian networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Improving Computation for Hierarchical Bayesian Spatial Gaussian Mixture Models with Application to the Analysis of THz image of Breast Tumor
… Carlo methods (MCMC) and their application in Bayesian inference. In particular, we discuss the Metropolis-Hastings and conjugate Gibbs algorithms and explore the computational underpinnings of these methods. The second chapter discusses how to incorporate spatial autocorrelation in linear a …
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Hierarchical Bayesian Spectro-temporal Models of Type Ia Supernovae in the Optical and Near-Infrared: Understanding the Properties of Dust in Supernova Host Galaxies
… this thesis, I develop and deploy a robust new hierarchical Bayesian framework for modelling the spectral energy distributions (SEDs) of SNe Ia in the optical and near-infrared (NIR). I apply this framework to detailed studies of the dust in SN Ia host galaxies – particularly the distribution of …
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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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The acquisition of inductive constraints
… almost certainly learned. This thesis presents a hierarchical Bayesian framework that helps to explain the nature, use and acquisition of inductive constraints. Hierarchical Bayesian models include multiple levels of abstraction, and the representations at the upper levels place constraints on the …
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The effect of locational uncertainty in geostatistics
… without locational error;We also develop a hierarchical Bayesian model to incorporate locational uncertainty into spatial data analysis. We use Markov chain Monte Carlo techniques to draw from the posterior distribution of the large-scale trend parameters, the covariance-model parameters, …
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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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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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