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.
Results
Showing 1 to 10 of 10 for “"Posterior Sampling"”.
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Bayesian nonparametric learning with semi-Markovian dynamics
… Hidden semi-Markov Model (HDP-HSMM) and develop posterior sampling algorithms for efficient inference. We also develop novel sampling inference for the Bayesian version of the classical explicit-duration Hidden semi-Markov Model. We demonstrate the utility of the HDP-HSMM and our inference …
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Transport maps for accelerated Bayesian computation
… observational data. Characterizing a Bayesian posterior probability distribution can be a computationally challenging undertaking, however, particularly when evaluations of the posterior density are expensive and when the posterior has complex non-Gaussian structure. This thesis addresses these …
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A hierarchical Bayesian calibration framework for quantifying input uncertainties in thermal-hydraulics simulation models
… Analysis (SA), surrogate model construction, and posterior sampling by Markov Chain Monte Carlo (MCMC) algorithms. SA aims at screening out input parameters that have low impacts on Quantity of Interests (QoI), surrogate models are developed to replace the computationally expensive TH codes and …
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Statistical learning for decision making : interpretability, uncertainty, and inference
… transaction data with stockouts. We show how posterior sampling can be used to directly incorporate model uncertainty into the decisions that will be made using the model. In the third part of the thesis we propose a method for aggregating relevant information from across the Internet to …
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Guiding diffusion generative models with applications to inverse problems
Conditional sampling via denoising diffusion models (DDMs) has received significant interest in generative modelling for their scalability, improved sample quality, and versatile application. These models are widely used in scientific and industrial settings, where they leverage latent …
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Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
… algorithm of deep learning---to perform posterior sampling in linear models and their convex duals: Gaussian processes. With this, we turn back to linearised neural networks, finding the linearised Laplace approximation to present a number of incompatibilities with modern deep learning …
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Enhancing Capabilities of Assistive Robotic Arms: Learning, Control, and Object Manipulation
… robot's trajectory. We introduce an approximate posterior sampling solution that builds the robot's motion one waypoint at a time. Our simulations and real-world experiments show that this approach achieves faster learning than state-of-the-art baselines. Finally, to address the challenge of …
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Score Estimation for Generative Modeling
… a novel algorithm inspired by maximum a posteriori estimation. This approach combines multiple levels of Gaussian smoothing with an α-posterior, enabling effective signal separation using only independent priors for the sources. We demonstrate the effectiveness of this method through its …
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Statistical Analysis of Structured High-dimensional Data
… represented by an undirected graph. We perform posterior sampling through Markov chain Monte Carlo algorithms. The practical performance of the proposed approach is demonstrated through simulations as well as near-infrared and sonar data. The second part of this dissertation focuses on …
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Denoising for deuterium magnetic resonance spectroscopic imaging based on posterior-score-guided subspace modeling
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01