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 29 for “"Bayesian Methodology"”.
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Doodlebugging: A Bayesian Methodology of Design
… a technology by incorporating it into a design methodology, and what I will refer to as divination in this project includes inferring meaning from a dowsing wand, traditionally used to find or locate water, in order to inquire about the design that is in equilibrium with the flow and direction …
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Bayesian methodology for integrating multiple data sources and specifying priors from predictive information
… and latent parameters is the starting point for Bayesian inference. It is challenging to specify such a model so that it both accurately describes the phenomena being studied, and is compatible with the available data. In this thesis we address challenges to model specification when we have …
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Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data
… are assumed normally distributed we use the Bayesian Model Averaging method to average the models, select the highest probability model and do variable assessment. Accuracy in calculating the posterior model probabilities using the Laplace approximation and an approximation based on the …
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Bayesian analysis for categorical survey data
In this thesis, we develop Bayesian methodology for univariate and multivariate categorical survey data. The Multinomial model is used and the following problems are addressed. Limited information about the design variables leads us to model the unknown design variables taking into account the …
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Gravitational Lensing in the Solar Neighbourhood and Towards the Milky Way Bulge
… the Galactic bulge. In both cases, I develop a Bayesian methodology to characterize the microlensing signals. In Chapter 1, I review the history of finding microlensing events both by predicting stellar alignments and by monitoring millions of stars. I describe two uses for these types of …
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Bayesian methods in non-clinical pharmaceutical statistics.
… research papers investigating the application of Bayesian methods to pharmaceutical non-clinical statistics. In the first paper, we present an application of Bayesian assurance and sample size determination to the manufacturing process validation life-cycle. In particular, we show how the …
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Multi-Satellite Remote Sensing of Land-Atmosphere Interactions: Advanced Data-Driven Methodologies for Passive Microwave Retrievals of Flood and Precipitation
… presents a multi-satellite multi-sensor Bayesian methodology for prognostic detection of two key components in the terrestrial water cycle: (1) the extent of flooded regions at a sub-daily basis, which improves the flood forecasting by identifying the soil saturated zones, and (2) the …
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Modelling credit spreads in an illiquid South African corporate debt market
… the bond issuer. This key feature is used in a Bayesian methodology to impute missing credit spread data for calibration, for more meaningful inference. On sparse simulated data and market observed credit spread time series, the model proves to deliver an improved quality of the estimations, …
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Modeling Error in Geographic Information Systems
… vector data. In particular, I am incorporating Bayesian methodology into the currently popular G-band error model through the inclusion of a prior distribution on point locations. This has the advantage of working well with a small number of points, and being able to synthesize information from …
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Bayesian estimation of restricted latent class models: Extending priors, link functions, and structural models
… have led to the creation of exploratory methodology that is able to infer the Q matrix without expert intervention. Within this thesis, we seek to extend and improve upon existing exploratory techniques and applications. We begin by developing novel Bayesian methodology that uses a less …
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Bayesian Linear Modeling in High Dimensions: Advances in Hierarchical Modeling, Inference, and Evaluation
… like this, the languages of linear modeling and Bayesian statistics appeal because they provide interpretability, coherent uncertainty, and the capacity for information sharing across related datasets. But at the same time, high dimensionality introduces several challenges not solved by existing …
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A study of Population MCMC for estimating Bayes Factors over nonlinear ODE models
… of new model hypotheses. Towards that end, Bayesian methodology provides an ideal framework for tackling such challenges, and in particular offers a means of objectively comparing competing plausible models through the estimation of Bayes factors. There are, however, formidable obstacles …
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Models for Data Analysis in Accelerated Reliability Growth
… failure mode and test covariates. We develop a Bayesian methodology to analyze the data by assigning a prior distribution to each model parameter, developing a sequential Metropolis-Hastings procedure to sample the posterior distribution of the model parameters, and deriving closed form …
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Efficient Bayesian analysis of spatial occupancy models
… about species distribution and occurrence. Bayesian methodology is a popular framework used to model the relationship between species and environmental variables. In this dissertation we develop a Gibbs sampling method using a logit link function in order to model posterior parameters of the …
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Bayesian Integrative Analysis of Omics Data
… like cancer. This dissertation focuses on Bayesian methodology establishment in integrative analysis of radiogenomics and pathway driver detection applied in cancer applications. We initially present Radio-iBAG that utilizes Bayesian approaches in analyzing radiological imaging and …
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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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Proactive Decision Support Tools for National Park and Non-Traditional Agencies in Solving Traffic-Related Problems
… approach is a machine learning classification methodology known as "decision tree." In this study, probabilities of stopping at attractions are predicted based on GPS tracking data that include entrance location, time of day and stopping at attractions. Chapter 5 considers many of the previous …
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Bayesian Methods for the Design and Analysis of Cluster Randomised Controlled Trials
… sample size calculation, and in analysis. The methodology underpinning the cluster randomised design is now well-established in the statistical literature. However, the overwhelming majority of methodological developments to date have been within the frequentist paradigm, and as such, there is …
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Statistical Inference and Learning for Stochastic and Partial Differential Equations
… distribution over the PDE solution. Taking a Bayesian approach in both cases, we aim to quantify the uncertainty on the solution via the posterior distribution, whilst estimating unknown physical model parameters. Our second contribution considers the case of a known observation model. We …
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