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 16 of 16 for “"Bayesian machine learning"”.
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Advances in Bayesian Machine Learning: From Uncertainty to Decision Making
Bayesian uncertainty quantification is the key element to many machine learning applications. To this end, approximate inference algorithms are developed to perform inference at a relatively low cost. Despite the recent advancements of scaling approximate inference to “big model $\times$ big data” …
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Simulation-based Bayesian machine learning methods for Cosmology and beyond
… 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 the method is a general-purpose algorithm applicable beyond Cosmology. This thesis is …
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Strong and weak principles of Bayesian machine learning for systems neuroscience
… Inspiration for such tools can be found in the Bayesian machine learning literature, which provides a set of principled techniques that allow us to perform inference in complex problem settings with large parameter spaces. When applied to neural population recordings, we propose that these …
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Ship design through Axiomatic Design approach, sustainable engineering principles and artificial intelligence methods
… known sustainable engineering principles. The Bayesian machine learning technique is proposed as a data-driven method for calculating the probability of achieving specific sustainability-related functional requirements, selecting the best design parameters among the proposed alternatives, and …
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Probabilistic machine learning for the elimination of thermoacoustic instabilities
… The current thesis demonstrates how Bayesian machine learning techniques may be of benefit when modeling, designing against and trying to avoid thermoacoustic instabilities. We show that Bayesian Neural Network can be used to assimilate model parameters from flame data and make our …
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Algorithms for Understanding and Fighting Infectious Disease
… Next, this thesis will show how state-of-the-art Bayesian machine learning can explore complex biological spaces to search for new therapies that fight infectious disease. Finally, this thesis develops neural language models that can predict how pathogens mutate to evade human immunity, …
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Active Inference in Multi-Objective Dynamic Environments
… process theory and more classical agent-based machine learning. However, due to the relative recency of the theory, there are still many areas of comparison and evaluation to explore. This dissertation aims to investigate Active Inference's algorithmic capacity to solve more complex …
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Scalable Approximate Inference and Model Selection in Gaussian Process Regression
… likelihoods are one of only a handful of Bayesian models where inference can be performed without the need for approximation. However, a frequent criticism of these models from practitioners of Bayesian machine learning is that they are challenging to scale to large datasets due to the …
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Neural response variability in the navigational system of the brain
… Non-renewal (NPNR) process. By leveraging Bayesian machine learning, we obtain a data-efficient yet flexible model for neural activity that can flexibly model neural response statistics beyond the conventional rate-based Poisson framework. We apply the UCM and NPNR process to various …
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Precipitation prediction over High Mountain Asia using Gaussian processes
… discusses the application of probabilistic machine learning to better understand and predict precipitation in this area. Probabilistic methods quantify our knowledge of precipitation and improve decision-making under uncertainty. This work centres around one such method, Gaussian processes. …
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Batch and Online Implicit Weighted Gaussian Processes for Robust Novelty Detection
… to compensate for outliers in the training data. Bayesian kernel methods, and in particular GPs, have been used to solve a variety of machine learning problems, equating or exceeding the performance of other successful techniques. That is the case of a recently proposed approach to GP-based …
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Measuring Machine Learning Model Uncertainty with Applications to Aerial Segmentation
<p>Machine learning model performance on both validation data and new data can be better measured and understood by leveraging uncertainty metrics at the time of prediction. These metrics can improve the model training process by indicating which training data need to be corrected and what part of …
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Information Analysis of Spatiotemporal Data Stream–Models, Algorithms and Evaluations
… dynamics data are then processed by a proposed Bayesian dynamics model for real-time crowd tracking and prediction. These models are validated and compared with benchmarks by simulation studies and a field test. In the WDS application, a penalized free-knot B-spline model is proposed to model …
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Ambient seismic noise imaging using trans-dimensional and machine learning techniques with application to Borneo and Iceland
… advances have led to the use of sophisticated Bayesian trans-dimensional inversion schemes, which allow for variable resolution and can estimate posterior uncertainty, though at increased computational cost compared to traditional linearised methods. In the first part of this thesis, I leverage …