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 22 for “"Bayesian model selection"”.
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Predictive Alternatives in Bayesian Model Selection
Model comparison and hypothesis testing is an integral part of all data analyses. In this thesis, I present two new families of information criteria that can be used to perform model comparison. In Chapter 1, I review the necessary background to motivate the thesis. Of particular interest is the …
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Bayesian model selection with applications to radio astronomy
… of two main parts, both of which focus on Bayesian methods and the problem of model selection in particular. The first part investigates a new approach to computing the Bayes factor for model selection without needing to compute the Bayesian evidence, while the second part shows, through an …
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Three essays in macroeconomic forecasting using Bayesian model selection
This thesis explores several aspects of Bayesian model selection in time series forecasting of macroeconomic variables. The contribution is provided in three essays. In the first essay (Chapter 2) I forecast quarterly US inflation based on the generalized Phillips curve using econometric methods …
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Bayesian Model Selection in terms of Kullback-Leibler discrepancy
… article we investigate and develop the practical model assessment and selection methods for Bayesian models, when we anticipate that a promising approach should be objective enough to accept, easy enough to understand, general enough to apply, simple enough to compute and coherent enough to …
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Detecting episodes of star formation using Bayesian model selection.
Bayesian model comparison is a data-driven method to establish model complexity. In this dissertation we investigate its use in detecting multiple episodes of star formation from the analysis of the Spectral Energy Distribution (SED) of galaxies. This method is validated by simulating galaxy …
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Topics in Bayesian sample size determination and Bayesian model selection.
This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the …
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Bayesian Model Selection for Spatial Data and Cost-constrained Applications
Bayesian model selection is a useful tool for identifying an appropriate model class, dependence structure, and valuable predictors for a wide variety of applications. In this work we consider objective Bayesian model selection where no subjective information is available to inform priors on model …
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Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection
Standard linear modeling approaches make potentially simplistic assumptions regarding the structure of categorical effects that may obfuscate more complex relationships governing data. For example, recent work focused on the two-way unreplicated layout has shown that hidden groupings among the …
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Computational Bayesian techniques applied to cosmology
… 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A …
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Hierarchical Gaussian Processes for Spatially Dependent Model Selection
In this dissertation, we develop a model selection and estimation methodology for nonstationary spatial fields. Large, spatially correlated data often cover a vast geographical area. However, local spatial regions may have different mean and covariance structures. Our methodology accomplishes three …
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Bayesian smoothing spline models and their application in estimating yield curves
… properties. In this dissertation, a class of Bayesian smoothing spline models is developed for the yield curve estimation under different scenarios. These include the Bayesian smoothing spline model for estimating the Treasury yield curves, the Bayesian multivariate smoothing spline model for …
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Bayesian generalized additive model selection
Generalized additive models (GAMs) offer a parsimonious, flexible and interpretable framework for regression, particularly when handling a large numbers of candidate predictors. This thesis addresses the GAM variable selection problem: categorizing each candidate predictor's effect type to be …
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Responsibly Emboldening Predictions via Boldness-Recalibration
… The purpose of this work is to develop a Bayesian model selection-based approach to assess calibration, and a strategy for boldness-recalibration that enables practitioners to responsibly embolden predictions subject to their required level of calibration. Specifically, we allow the user …
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SALT spectroscopy and classification of supernova spectra using Bayesian techniques
… candidates. These were classified using a new Bayesian Supernova spectra classifier, SuperNovaMC, that we developed to address limitations with existing algorithms. SuperNovaMC simultaneously finds the best fitting supernova and host galaxy using Bayesian model selection, fitting the entire …
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Bayesian Methods and Machine Learning in Astrophysics
This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a …
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New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection
… 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. We explore …
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Bayesian Approach Dealing with Mixture Model Problems
… focus on two research topics related to mixture models. The first topic is Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions, and the second topic is Bayesian Model Selection for Nonlinear Mixed Effects Model. In the first topic, we consider a finite mixture …
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Bayesian modelling of nuclear fusion experiments
Bayesian probability theory as a general framework for scientific modelling and inference is introduced and applied to nuclear fusion experiments in order to provide consistent inference solutions given multiple heterogeneous data sets. Fusion plasmas are complex physical systems, in which charged …
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Online Joint Identification of Structural Dynamic Response Anomalies and Structural Damage Using Limited Data
… degrade data reliability; the lack of adaptive model updates restricts performance under changing environmental and operational conditions; and accurate damage diagnosis is often impeded by the scarcity of labeled damage data, which limits model generalizability and diagnostic accuracy. To …
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