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 35 for “"Bayesian modelling"”.
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Bayesian modelling of nuclear fusion experiments
Bayesian probability theory as a general framework for scientific modelling and inference is introduced and applied to nuclear fusion experiments in order to provide consistent inference solutions given multiple heterogeneous data sets. Fusion plasmas are complex physical systems, in which charged …
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Nonparametric Bayesian Modelling in Machine Learning
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space. Originally introduced …
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BMEA: Bayesian Modelling For Exon Array Data
… proved challenging. In this work a novel method, Bayesian Modelling for Exon Arrays (BMEA), is described which shows an improvement in performance over previous approaches, and fits a more appropriate model for each gene using an MCMC process. Applying BMEA to an in-house dataset contrasting …
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Bayesian modelling of mixed outcome types using random effect.
… In this dissertation we develop several Bayesian models for analyzing associated discrete and continuous responses simultaneously using random effects. We also extend these models to overcome the bias in parameter estimation due to ignorance of skewness of the continuous response, a …
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Bayesian Modelling of Galaxy Clusters with the Arcminute Microkelvin Imager
… observed with the SA are analysed in a fully Bayesian manner with the in-house software package McAdam, using a parametrised model for the cluster whilst simultaneously modelling the point source environment using priors based on LA estimates. The detection of clusters is reported based on …
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Data Analysis in Global 21cm Experiments: Physically Motivated Bayesian Modelling Techniques
… thesis primarily investigates the application of Bayesian data analysis techniques to global 21cm cosmology, to aid in overcoming two of the most prominent difficulties in detecting a sky-averaged ('global') 21cm signal: the presence of foregrounds around four orders of magnitude brighter than the …
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Bayesian modelling of music: algorithmic advances and experimental studies of shift-invariant sparse coding
… In order to learn model parameters, we use<br/>Bayesian statistical methods, however, analytic solutions to this learning<br/>problem are not available and approximations have to be introduced. In<br/>this thesis we study three approximations, one based on an analytical<br/>integral …
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Bayesian modelling and sampling strategies for ordering and clustering problems with a focus on next-generation sequencing data
… of longitudinal information. I developed a new, Bayesian, way of reconstructing this information computationally, sampling orders efficiently using MCMC on a space of permutations. This Bayesian approach provides novel insights into biological phenomena and experimental artefacts. The second part …
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Mathematical analysis of uncertainty in machine learning and deep learning
… is involved in many real-world situations. The Bayesian modelling can handle such uncertainty in machine learning community. However, the traditional deep learning model fails to show uncertainty for its outputs. Recently, at the intersection of the Bayesian modelling and deep learning, a new …
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Microfinance and Mobile phones: an economic analysis of the perceptions, management and measurement of environmental risk in rural Kenya
… This data is analysed within a pioneering Bayesian modelling framework to track how risk preferences update given changing environmental contexts. The study identifies a notable reduction in risk aversion post-harvest as well as evidence of increased levels of localised risk aversion in …
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Bayesian Learning for Data-Efficient Control
… 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 algorithm, which …
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Gravitational Wave prediction from Galactic binary populations for LISA
… where LISA is most sensitive. By employing Bayesian modelling approaches, we explore different functional forms of energy spectral densities, such as power-law, broken power-law models, and single-peak models, and address the challenges in accurately characterising GW backgrounds. The …
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Non-parametric Bayesian models for structured output prediction
… must be modelled. 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 …
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Efficiencies of small-scale sugarcane growers in the King Cetshwayo District Municipality of KwaZulu-Natal
… for 38 small-scale growers. Furthermore, the Bayesian Modelling Average technique (BMA) investigated policy-related sources of small-scale
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Quantifying expression variability in single-cell RNA sequencing data
… variability increases during ageing. I used a Bayesian modelling framework to quantify mean expression and transcriptional variability but due to a strong confounding effect between these two parameters, variability analysis was restricted to genes that are similarly expressed across the tested …
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Behavioural disinhibition in the syndromes associated with frontotemporal lobar degeneration
… with a FTLD syndrome and 20 healthy controls. Bayesian modelling of a response inhibition task was used to quantify behavioural disinhibition. Both neurotransmitters were reduced in the frontal cortex, but not occipital cortex, of patients compared to controls. Glutamate and GABA concentrations …
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Approximate Inference: New Visions
… confidence. Powered by the rules of probability, Bayesian inference is the gold standard method to perform coherent reasoning under uncertainty. It is generally believed that intelligent systems following the Bayesian approach can better incorporate uncertainty information for reliable decision …
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Exogeology as Revealed by Polluted White Dwarfs
… the data is a highly non-trivial exercise. Bayesian modelling is a powerful method to disentangle the most likely explanation from the myriad of possibilities. This approach reveals evidence that core formation and volatile loss, which shape Solar System bodies such as Earth, also occur in …
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Resolving abrupt palaeoenvironmental changes in a lake sediment sequence from Ioannina, northwest Greece
… reworking in lake environments are examined. Bayesian modelling, which incorporates the new tephra ages with earlier radiocarbon ages, extends the I-08 core chronology back to ca. 46 ka BP. New, centennial-scale palaeoenvironmental analysis of the I-08 core is presented, spanning the section …
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Benchmarking methods for label-free quantitative proteomics data analysis
… LFQ data analysis tools and highlight how Bayesian modelling paradigms can be used to address some of the challenges in LFQ data analysis. In Chapter 1, I will describe the motivation behind my work and discuss the context and scope of this thesis. In Chapter 2, I will outline the …
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