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 100 for “"Bayesian models."”.
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Bayesian models for visual information retrieval
… probability of retrieval error. This leads to a Bayesian architecture that is shown to generalize a significant number of previous recognition approaches, solving some of the most challenging problems faced by these: joint modeling of color and texture, objective guidelines for controlling the …
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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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Uncertainty quantification in machine learning with Bayesian models
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Towards Improved Variational Inference for Deep Bayesian Models
… but a few. However, it is well-known that deep models trained via maximum likelihood estimation tend to be overconfident and give poorly-calibrated predictions. Bayesian deep learning attempts to address this by placing priors on the model parameters, which are then combined with a likelihood to …
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Non-parametric Bayesian models for structured output prediction
… must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maintaining the advantages of Bayesian modelling. In this …
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Dynamic Bayesian models for modelling environmental space-time fields
… implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to …
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Bayesian models for screening and diagnosis of pulmonary disease
Pulmonary and respiratory diseases comprise a large proportion of the global disease burden, responsible for both mortality and disability, with the most common ailments being asthma, chronic obstructive pulmonary disorder (COPD), and allergic rhinitis (AR). This burden is especially concentrated …
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Improving information retrieval with textual analysis : Bayesian models and beyond
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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The "Fair" Triathlon: Equating Standard Deviations Using Non-Linear Bayesian Models
… is underweighted. We present a nonlinear Bayesian model for triathlon finishing times that models time and standard deviation of time as a function of distance. We use this model to create "fair" triathlons by equating the standard deviations of the times taken to complete the swimming, …
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Hierarchical Bayesian Models for Investigating Astrophysical Systematics in Type Ia Supernova Cosmology
… 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 development of …
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Bayesian models for unmeasured confounder in the analysis of time-to-event data.
… of time-to-event data. We also provide both the Bayesian parametric and the semi-parametric "twin regression" approaches with distributional assumptions of an unmeasured confounding variable, and then we compare them with the naive model. This assumes we ignore the effect of the unmeasured …
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Topics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination.
Researchers often analyze data assuming models with constrained parameters. Order constrained parameters are of particular interest. In this dissertation, we examine three Bayesian models which incorporate ordered parameters. We investigate ordered differential response misclassification in a …
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A comparative study of the effectiveness of two Bayesian models for predicting the academic successes of selected allied health students enrolled in the comprehensive community college
… Since neither the classical statistical models which utilize correlations, regression, discriminate analysis, etc. nor the counselor-selection models have typically utilized all the information regarding a student, the need for more efficient and effective guidance-selection models was …
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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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Geography: Its Place in Higher Education Enrollment
… 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 distribution or Bayesian …
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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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Sampling in human cognition
Bayesian Decision Theory describes optimal methods for combining sparse, noisy data with prior knowledge to build models of an uncertain world and to use those models to plan actions and make novel decisions. Bayesian computational models correctly predict aspects of human behavior in cognitive …
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Bayesian Model Selection in terms of Kullback-Leibler discrepancy
… 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 interpret. We mainly restrict attention to the …
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