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 14 of 14 for “"high-dimensional inference"”.
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High Dimensional Inference for Semiparametric Models
In the literature, high dimensional inference refers to statistical inference when
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High-Dimensional Inference with Heterogeneous Data
… and statistical challenges for traditional inference methods. A core assumption that underpins much of statistical theory and modelling is that the data are realisations of exchangeable random variables. While important, this limited setting falls short of capturing the complexities inherent …
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Geometric methods in computational optimal transport and high-dimensional inference
… of computational optimal transport and high-dimensional inference through four main contributions, each exploring fundamental connections between geometric structure and algorithmic efficiency. First, a refined analysis of the Sinkhorn algorithm’s convergence properties via the Hilbert …
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Applied stochastic Eigen-analysis
… of the theory of large random matrices to high-dimensional inference problems when the samples are drawn from a multivariate normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that …
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Geometric aspects of uncertainty quantification in high-dimensional statistics
In high-dimensional statistics, uncertainty quantification is often of its own mathematical interest and complexity. We discuss phenomena that are particular to high-dimensional inference tasks, which closely relate to the need to `adapt' to hidden lower-dimensional structures that are not directly …
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Problems in high-dimensional mediation analysis
… in linear mediation models in the presence of high-dimensional mediators: estimating and inference for the indirect effect, power analysis for testing total effect, and estimation for the proportion of indirect effect.
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Deep Generative Models and Biological Applications
<p>High-dimensional probability distributions are important objects in a wide variety of applications. </p><p>Generative models provide an excellent manipulation method for training from rich available unlabeled data set and sampling new data points from underlying high-dimensional probability …
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Applied stochastic eigen-analysis
… of the theory of large random matrices to high-dimensional inference problems when the samples are drawn from a multivariate normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that …
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Multiple Testing Embedded in an Aggregation Tree With Applications to Omics Data
… I have developed computational methods for high dimensional inference, motivated by the analysis of omics data. This dissertation is divided into two parts. The first part of this dissertation is motivated by flow cytometry data analysis, where a key goal is to identify sparse cell …
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Methods and Theory for Nonparametric Inference In High-dimensional Settings
… addresses nonparametric estimation and inference problems of graphical modeling, linear association assessment, and matrix completion. First, we introduce a flexible framework for nonparametric graphical modeling. We propose three nonparametric measures of conditional dependence, which …
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Gradient-based dimension reduction for Bayesian inverse problems and simulation-based inference
Inference is a pervasive task in science and engineering applications. The Bayesian approach to inference facilitates informed decision making by quantifying uncertainty in parameters and predictions, but can be computationally demanding. This thesis focuses on Bayesian methods for inverse problems …
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Probabilistic modeling and Bayesian inference via triangular transport
Probabilistic modeling and Bayesian inference in non-Gaussian settings are pervasive challenges for science and engineering applications. Transportation of measure provides a principled framework for treating non-Gaussianity and for generalizing many methods that rest on Gaussian assumptions. A …
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Stochastic Dynamically Orthogonal Modeling and Bayesian Learning for Underwater Acoustic Propagation
… implement algorithms for the Bayesian nonlinear inference and learning of the ocean, bathymetry, seabed, and acoustic fields and parameters using sparse data; and (3) demonstrate the new methodologies in a range of underwater acoustic applications and real sea experiments, showcasing new …
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Simulation-based Bayesian machine learning methods for Cosmology and beyond
… by the limitations of likelihood-based Bayesian inference in sky-averaged 21-cm Cosmology. Moreover, PolySwyft merges nested sampling and neural ratio estimation into a general Bayesian framework, and the method is a general-purpose algorithm applicable beyond Cosmology. This thesis is divided …