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 17 of 17 for “"Hierarchical Bayesian Models"”.
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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
… detect abnormal activities, and learn the models of semantically meaningful scene structures, such as paths commonly taken by objects. In medical imaging, some issues similar to learning motion patterns arise. Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is one of the first methods …
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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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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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Framework theories in science
… or concrete hypotheses. The first chapter uses hierarchical Bayesian models to show that the assessment of higher level theories may proceed by the same Bayesian principles as the assessment of more specific hypotheses. It thus shows how the evaluation of higher level theories can be …
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Probabilistic data analysis with probabilistic programming
… introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel …
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On the nature and origin of intuitive theories : learning, physics and psychology
This thesis develops formal computational models of intuitive theories, in particular intuitive physics and intuitive psychology, which form the basis of commonsense reasoning. The overarching formal framework is that of hierarchical Bayesian models, which see the mind as having domain-specific …
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An Applied Bayesian Approach to Network Meta-Analysis
… journals revealed a lack of presentations of Bayesian models within a network meta-analysis framework and thus motivated further research into this combined area of study. The development of four hierarchical Bayesian models applicable to the field of network meta-analysis are presented. Two …
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Spatial Temporal Analysis Using Hierarchical Bayesian Approach: Effects of Climate Variability on Primary Production of Deciduous Forests of the Northeastern U.S.
… data from the FLUXNET monitoring network. I used Hierarchical Bayesian models to account for climate variability (anomalies) and short-term weather events (such as drought, heat) to quantify the effects of climatic variability on forest ecosystem productivity. I also explored different lagged …
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Computational foundations of human social intelligence
… thesis develops formal computational cognitive models of the social intelligence underlying human cooperation and morality. Human social intelligence is uniquely powerful. We collaborate with others to accomplish together what none of us could do on our own; we share the benefits of …
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Propagation and monitoring of freshwater mussels released into the Clinch and Powell rivers, Virginia and Tennessee
… increased above 98%. I developed a set of hierarchical Bayesian models incorporating individual variations, seasonal variations, periodic growth stages and growth cessation to estimate survival, detection probability and growth of released mussels in a changing environment. Mussels of E. …
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Hierarchical Inference in Gaussian Processes
Hierarchical modelling is a fundamental theme in probabilistic machine learning which relies on the Bayesian interpretation on probability. The starting requirement for these models is that all unknowns are treated as random variables with their own respective probability distributions. The central …
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Bayesian population dynamics modeling to guide population restoration and recovery of endangered mussels in the Clinch River, Tennessee and Virginia
… mussels; (3) age-specific natural mortality. A Bayesian approach was used to analyze the age-structured models and a Bayesian model averaging approach was applied to average the results by weighting each model using the deviance information criterion (DIC). A risk assessment was conducted to …
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Reliable Inference from Unreliable Agents
… and the behavior is statistically modeled using hierarchical Bayesian models. The implications of such modeling on the design of large human-machine systems is discussed. Furthermore, an error-correcting codes based scheme is proposed to improve system performance in the presence of unreliable …
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Using statistical learning approaches to understand trends and variability of tornadoes across the continental United States
… and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern …