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 40 for “"Probabilistic Modelling"”.
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Probabilistic Modelling in Function Space
… models, a leading technique in deep generative modelling. The main contribution in this section is the extension of diffusion models to function spaces. We demonstrate the potential of this approach as a viable alternative to Gaussian processes for complex data, especially when ample data is …
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ON THE PROBABILISTIC MODELLING OF PAIN
… The aim of this study is to develop a probabilistic and computational model of pain that aligns with both behavioural data and neurobiological constraints, aspiring to represent pain at various levels. This includes incorporating perceptual, affective, motivational, and social aspects …
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Relaxing assumptions in deep probabilistic modelling
… this success, deep learning and deep generative modelling have progressively been applied across a broader range of increasingly demanding applications, as well as in safety-critical domains such as healthcare. However, existing models are reliant upon restrictive theoretical assumptions, …
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Probabilistic Modelling of Sensitivity in Fire Simulations
The objective of this thesis is to apply probabilistic sensitivity analyses to the emerging field of toxic hazard simulation in fire science. Fire simulation based on computational fluid dynamics (CFD) plays an important role in performance-based fire design. However, the thermal-physical process …
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Probabilistic Modelling of Replication Fidelity in Eukaryotic Genomes
Eukaryotic DNA replication is composed of a complex array of molecular biological activities compounded by the pressure for faithful replication in order to maintain genetic and genomic integrity. The constraints governing DNA replication biology is of fundamental importance to understand the …
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Probabilistic modelling for durability design of reinforced concrete structures
… to create a framework for the development of a probabilistic model for durability design of reinforced concrete (RC) structures in South African marine conditions. Durability design of RC structures is mainly concerned with ensuring the ability of the concrete to resist the penetration of …
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Probabilistic modelling of cellular development from single-cell gene expression
The recent technology of single-cell RNA sequencing can be used to investigate molecular, transcriptional, changes in cells as they develop. I reviewed the literature on the technology, and made a large scale quantitative comparison of the different implementations of single cell RNA sequencing to …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
… and priors affect the inductiven biases of probabilistic models, and our ability to learn and make inferences from data. Specifically we present theoretical analyses alongside algorithmic and modelling advances in three areas of probabilistic machine learning: sparse Gaussian process …
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Probabilistic modelling of somatic alterations in bulk tissue and single cells using repeat DNA
… research. In this thesis, I develop conliga; a probabilistic generative model and associated inference algorithms to infer relative copy number from FAST-SeqS data at the amplicon level. I implement this method in R and C++ and provide the software as an open-source tool. By applying conliga and …
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Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence.
… multiple algorithms and drastically reducing the modelling time by multiple factors; proposing new fixed structure Bayesian network learning algorithms for node ordering search-space exploration. Finally, this work proposes real-world applications for the models based on current industry needs, …
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Multimodal Probabilistic Inference for Robust Uncertainty Quantification
… of research on uncertainty quantification is probabilistic modelling which is concerned with capturing model uncertainty by placing a distribution over the models which can be marginalized at test-time. This is especially useful in underspecified models which can have diverse near-optimal …
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Geometry and Uncertainty in Deep Learning for Computer Vision
… performance. Secondly, we introduce ideas from probabilistic modelling and Bayesian deep learning to understand uncertainty in computer vision models. We show how to quantify different types of uncertainty, improving safety for real world applications.
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Neurobiology of incremental speech comprehension
… Using a novel application of multidimensional probabilistic modelling combined with models from computational linguistics, I developed models of a variety of computational processes associated with accessing and processing the syntactic and semantic properties of sentences and tested these …
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Probabilistic on-line transportation problems with carrying-capacity constraints
… in real time. The study extends an existing probabilistic framework which has provided numerous insights about vehicle scheduling and routing problems since its inception. Additionally, the thesis provides algorithms and new probabilistic cost bounds, for optimal bipartite matchings between …
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Stochastic analysis of structures made of composite materials
A probabilistic methodology for the reliability analysis of composite rotor blades at the ply level was developed. The proposed methodology involves (i) the quantification of the uncertainties (physical, statistical and model) related to the material properties and the extreme aero-elastic loads …
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Bayesian Learning for Data-Efficient Control
… Data efficient learning critically requires probabilistic modelling of dynamics. Traditional control approaches use deterministic models, which easily overfit data, especially small datasets. We use probabilistic Bayesian modelling to learn systems from scratch, similar to the PILCO …
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Non-parametric Bayesian models for structured output prediction
… Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maintaining the advantages of Bayesian modelling. In this thesis, we develop NPB algorithms …
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Computational intelligent systems : evolving dynamic Bayesian networks
In this thesis, a new class of temporal probabilistic modelling, called evolving dynamic Bayesian networks (EDBN), is proposed and demonstrated to make technology easier so as to accommodate both experts and non-experts, such as industrial practitioners, decision-makers, researchers, etc. Dynamic …
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