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 8 of 8 for “"Bayesian inverse problems"”.
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PRACTICAL INVESTIGATIONS ON BAYESIAN INVERSE PROBLEMS
Inverse problems make up a challenging and practically important class of inference problems. Classical methods provide point estimates and confidence intervals which are asymptotically justified. As the computational power increased, however, ractitioners and researchers looked for better …
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Gradient-based dimension reduction for Bayesian inverse problems and simulation-based inference
… 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 governed by partial …
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Accelerating Bayesian Computation in Earth Remote Sensing Problems
Earth atmospheric remote sensing is an inverse problem that fits surface and atmospheric models to imaging spectrometer data and is critical to the analysis of the composition and biodiversity of the Earth surface. Current methods for remote sensing generally involve retrieving a point estimate of …
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Scalable Surrogates for Counts and Computer Experiments
… To address this gap, I propose a fully Bayesian, Vecchia-approximated, Poisson deep GP surrogate model. I demonstrate its improved predictive capability over competitors through multiple simulated examples. Further, I develop a novel, fully Bayesian framework for solving Bayesian inverse …
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An optimization based algorithm for Bayesian inference
In the Bayesian statistical paradigm, uncertainty in the parameters of a physical system is characterized by a probability distribution. Information from observations is incorporated by updating this distribution from prior to posterior. Quantities of interest, such as credible regions, event …
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Variational Inference and Probabilistic Models for Parametric Partial Differential Equations
… are ill-equipped in dealing with parametric problems as there is no information carry-over from one simulation to the next. This thesis is an attempt at adapting methods of probabilistic machine learning to create methodological advances in solving various problems relating to PDEs though …
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On the low-dimensional structure of Bayesian inference
The Bayesian approach to inference characterizes model parameters and predictions through the exploration of their posterior distributions, i.e., their distributions conditioned on available data. The Bayesian paradigm provides a flexible, principled framework for quantifying uncertainty, wherein …
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Computational Advancements for Solving Large-scale Inverse Problems
For many scientific applications, inverse problems have played a key role in solving important problems by enabling researchers to estimate desired parameters of a system from observed measurements. For example, large-scale inverse problems arise in many global problems and medical imaging problems …