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 49 for “"Bayesian modeling"”.
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Bayesian modeling of microwave foregrounds
… masks the CMB signal. Recent developments in modeling techniques may provide a better understanding of these foregrounds and allow improved constraints on current cosmological models. The method of nested sampling [16, 5], a Bayesian inference technique for calculating the evidence (the …
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Approximate Bayesian Modeling with Embedded Gaussian Processes
Bayesian inference plays an important role in uncertainty quantification and decision making in science and engineering applications. However, the presence of complex model components can significantly impact model accuracy and computational feasibility of Bayesian inference methods. We propose the …
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Bayesian Modeling of Complex High-Dimensional Data
… of this dissertation is to develop innovative Bayesian methodologies to enable effective and efficient knowledge discovery from complex high-dimensional data. It contains two parts—the development of computationally efficient functional mixed models and the modeling of data heterogeneity via …
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Bayesian modeling of manner and path psychological data
… The resulting information was used to design a Bayesian model which successfully predicts the human behavior in the psychological experiments that were conducted. Dynamic parameters were required to account for subjects revising their expected manner and path verb distributions during the course …
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Bayesian Modeling of a High Resolution Housing Price Index
… have correlated valuation dynamics. We propose a Bayesian nonparametric approach which builds on the framework of latent factor models to enable a flexible, data- driven method for inferring the clustering of correlated census tracts. We explore methods for scalability and parallelizability of …
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Bayesian Modeling for Isoform Identification and Phenotype-specific Transcript Assembly
… dissertation research, we have developed novel Bayesian approaches to infer alternative splicing mechanisms in biological systems using RNA sequencing data. Specifically, we focus on two research topics in this dissertation: isoform identification and phenotype-specific transcript assembly. For …
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Some Recent Advances in Non- and Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables
… thesis develops flexible non- and semiparametric Bayesian models for mixed continuous, ordered and unordered categorical data. These methods have a range of possible applications; the applications considered in this thesis are drawn primarily from the social sciences, where multivariate, …
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Uncertainty-aware Joint Physical Tracking and Prediction
… reasoning. To model this behavior, a structured Bayesian modeling and inference approach for joint tracking and prediction was developed that integrates perception, state estimation, and future prediction in a unified process. The approach, implemented via a Sequential Monte Carlo algorithm …
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Bayesian Nonparametric Modeling and Theory for Complex Data
… and methodological problems associated with Bayesian modeling of infinite dimensional `objects', popularly called nonparametric Bayes. The term `infinite dimensional object' can refer to a density, a conditional density, a regression surface or even a manifold. Although Bayesian density …
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Coupling Agent-Based Models with Bayesian Belief Networks in Social-Ecological Systems Modeling: The Role of Uncertainty in the Resilience of Semi-Arid Riparian Corridors in the Sonoran Desert
… methods; however, social-ecological system modeling approaches have overcome many of these difficulties. Uncertainty, particularly in regards to human decision-making, remains a challenge. I developed a series of models to test the use of Bayesian modeling techniques in addressing …
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From Oncogenesis to Immunotherapy: Mathematical Modeling of Heterogeneous Cancers
We utilize mathematical modeling to study two types of cancer with substantial mutational heterogeneity: chronic lymphocytic leukemia (CLL) and mismatch-repair deficient colorectal cancer (MMR-D CRC). First, to study the progression of CLL into an aggressive lymphoma, Richter’s Syndrome (RS), we …
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Bayesian Methods to Characterize Uncertainty in Predictive Modeling of the Effect of Urbanization on Aquatic Ecosystems
… are complex, multilayered, and interacting, modeling these effects presents many unique challenges, including: addressing and quantifying processes at multiple scales, representing major interrelated simultaneously acting dynamics at the system level, incorporating uncertainty resulting from …
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Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
… to conventional approaches of statistical modeling. Bayesian nonparametrics constitute a promising research direction, in that such techniques can fit the data with a model that can grow with complexity to match the data. In this dissertation we consider nonparametric Bayesian modeling with …
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Detecting intervention effects with a cognitive diagnostic model for learning trajectories
… models are introduced under the framework. A Bayesian modeling formulation is presented, and Gibbs sampling algorithm is proposed for parameter estimation. Simulation study results show that the proposed model provides accurate estimation of intervention effects and reliable recovery of …
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Cooperation and competition : modeling intention and behavior in dual-agent interactions
… two people. We used reinforcement learning and Bayesian modeling to create a mathematical representation of this mental model. Our model is comprised of a high-level planner that understands abstract social intentions, and it employs two low-level planners that perform cooperative and …
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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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Statistical Monitoring and Modeling for Spatial Processes
Statistical process monitoring and hierarchical Bayesian modeling are two ways to learn more about processes of interest. In this work, we consider two main components: risk-adjusted monitoring and Bayesian hierarchical models for spatial data. Usually, if prior information about a process is …
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Nonparametric Bayesian methods for supervised and unsupervised learning
I introduce two nonparametric Bayesian methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it …
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KINETIC ANALYSIS OF AUXIN-INDUCED TRANSCRIPTION FACTOR NETWORKS CONTROLLING LATERAL ROOT DEVELOPMENT IN ARABIDOPSIS THALIANA
… computationally with fuzzy logic and continuous Bayesian modeling techniques. The resultant transcription factor network was tested through qRT-PCR in the arf19-1 background and T-DNA mutant phenotype analysis. MIF1 and NF-YB2 are mis-expressed in the arf19-1 background. As bhlh122 and nf-yb2 …
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