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 55 for “"Bayesian Learning"”.
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High-dimensional Multimodal Bayesian Learning
… by these types of datasets. We develop three Bayesian methods: (1) Multilevel Network Recovery for Genomics, (2) Network Recovery for Functional data, and (3) Bayesian Inference in Transformer-based Models. Chapter 2 in our work examines a two-tiered data structure; to simultaneously explore …
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Bayesian learning for multi-agent coordination
… on these approaches by extending a principled Bayesian model into more challenging domains, using Bayesian networks to visualise specific cases of the model and thus as an aid in deriving the update equations for the system. One approach which has been shown to scale well for networked offline …
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Source Separation using Sparse Bayesian Learning
… linear systems, we propose utilizing Sparse Bayesian Learning and present our results on selected problems.
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Bayesian Learning for Data-Efficient Control
… to finance, to industrial processing, autonomous learning helps obviate a heavy reliance on experts for system identification and controller design. Often real world systems are nonlinear, stochastic, and expensive to operate (e.g. slow, energy intensive, prone to wear and tear). Ideally …
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Towards Better Representations with Deep/Bayesian Learning
<p>Deep learning and Bayesian Learning are two popular research topics in machine learning. They provide the flexible representations in the complementary manner. Therefore, it is desirable to take the best from both fields. This thesis focuses on the intersection of the two topics— enriching one …
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Efficient Sparse Bayesian Learning using Spike-and-Slab Priors
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly …
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The Pricing Strategy of a Bayesian Learning Monopolistic Insurer
… when meeting consumers for the first time. Using Bayesian learning a monopolistic insurer tries to learn a consumer's level of risk aversion. This paper shows that an insurer who learns in the two-type consumer model offers either separating contracts to the two different types of consumer or does …
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Hidden states, hidden structures: Bayesian learning in time series models
… for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work …
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Function-Space Bayesian Learning: from Gaussian Processes to Bayesian Deep Learning
Bayesian methods provide a general and principled framework to quantify and update beliefs based on prior knowledge and observed evidence. This thesis presents my works about function-space Bayesian learning, whose priors and posteriors are specified and computed over the function-space. This …
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Sequential mastery detection and Bayesian learning promotion under cognitive diagnosis models
E-learning assessments are becoming a common educational medium to instruct fine-grained skills in modern pedagogy. To accentuate the advantages of e-learning assessments, it is vital to automate the process of instruction and advancement in accordance with the learning progress of each individual. …
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Stochastic Dynamically Orthogonal Modeling and Bayesian Learning for Underwater Acoustic Propagation
… develop theory and 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, …
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Bayesian learning for high-dimensional nonlinear dynamical systems : methodologies, numerics and applications to fluid flows
… statistical inference, and machine learning have opened up new opportunities for utilizing data to assist, identify and refine physical models. In this thesis, we focus on Bayesian learning for a particular class of models: high-dimensional nonlinear dynamical systems, which have …
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Statistical Recursive Estimation Algorithms for Speaker Adaption
… maximum likelihood estimation and recursive Bayesian learning is first developed and applied to direct hidden Markov model parameter estimation. Then an online Bayesian learning technique is proposed for recursive maximum a posteriori estimation of tree-structured linear regression and affine …
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Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases
… is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will …
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Essay on beliefs and the macroeconomy
… it arises naturally as the limit of adaptive and Bayesian learning, and that it incorporates a version of the Lucas critique.
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Orbital Level Understanding of Adsorbate-Surface Interactions in Metal Nanocatalysis
… and O₂ reduction on Pt (5𝑑⁹6𝑠¹). We also employ Bayesian learning and the Newns-Anderson model to advance the fundamental understanding of adsorbate-surface interactions on metal nanocatalysts, paving the path toward adsorbate-specific tuning of catalysis.
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Integrated Technology and Product Design Decisions With Market-Based Learning
… consumer preferences through ""market-based learning"". The analysis is done within a context of a dynamic, stochastic model with Bayesian learning over a finite planning horizon. Analytical results are derived for some special cases of the model. More specifically, the impact of …
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Unsupervised Structure Induction for Natural Language Processing
… question answering, etc. Traditional supervised learning methods rely on manually labeled structures for training. Unfortunately, manual annotations are often expensive and time-consuming for large amounts of rich text. It has great value to induce structures automatically from unannotated …
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Models of intelligence operations
… cycle is modelled as a novel finite horizon Bayesian stochastic dynamic programming problem, namely the multi-armed bandit allocation (MABA) problem. The MABA framework models the efforts of a processor to search for intelligence items of the highest importance by making sequential samples …
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Detecting and Combining Programming Patterns
… used by guest programs using unsupervised Bayesian learning. Using these data structures, it can detect viruses with considerably better accuracy than ClamAV, a leading industry solution. Using high-level features makes Laika considerably more resistant to polymorphic worms, because it …
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