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 6 of 6 for “"simulation-based inference"”.
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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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Decoding Dark Matter Halos through the Lens of Machine Learning
… at galactic scales. While cosmological simulations and astrophysical surveys have made significant strides in constraining DM properties, upcoming surveys will generate terabytes of complex, high-dimensional data. It is thus imperative to develop new methodologies capable of interpreting …
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Simulation-based Bayesian machine learning methods for Cosmology and beyond
… developed algorithm PolySwyft. This sequential simulation- based nested sampler is motivated 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 …
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Advanced Deep Learning Methods for the Automatic Analysis of Radar Sounder Data
… by introducing an interactive framework based on unsupervised random walks and user-guided label propagation. This approach formulates feature learning as a random walk process on the radar image graph, effectively leveraging the strong horizontal correlations in the data. It allows …
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Cyber-Physical-Social Systems for Autonomous Defense: Enabling Mission-Centric, Adaptive, and Anytime Intelligence
… Intrusion Response Systems (IRS), and Anytime Inference (AIF), that collectively advance trustworthy decision-making under adversarial, uncertain, and resource-constrained conditions. Rather than functioning as isolated contributions, these tasks form a seamless progression: MIA quantifies …
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Neural Computation Through Synaptic Dynamics in Serotonergic Networks
… I developed and validated a set of likelihood-based inference tools to quantify the dynamics of synaptic ensemble composition throughout development. Second, I examined network computations in the serotonergic dorsal raphe nucleus through a dynamical lens, exploring the role of short-term …