Dissertations / Theses on the topic 'Bayesian modelling'
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Peeling, Paul Halliday. "Bayesian methods in music modelling." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/237236.
Full textStrimenopoulou, Foteini. "Bayesian modelling of functional data." Thesis, University of Kent, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544037.
Full textPolson, Nicholas G. "Bayesian perspectives on statistical modelling." Thesis, University of Nottingham, 1988. http://eprints.nottingham.ac.uk/11292/.
Full textBaker, Peter John. "Applied Bayesian modelling in genetics." Thesis, Queensland University of Technology, 2001.
Find full textHabli, Nada. "Nonparametric Bayesian Modelling in Machine Learning." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34267.
Full textDelatola, Eleni-Ioanna. "Bayesian nonparametric modelling of financial data." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589934.
Full textYan, Haojie. "Bayesian spatial modelling of air pollution." Thesis, University of Bath, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541668.
Full textBrown, G. O. "Model discrimination in Bayesian credibility modelling." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596996.
Full textKheradmandnia, Manouchehr. "Aspects of Bayesian threshold autoregressive modelling." Thesis, University of Kent, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303040.
Full textSmith, Elizabeth. "Bayesian modelling of extreme rainfall data." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424142.
Full textChai, High Seng. "Bayesian modelling with skew-elliptical distributions." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432726.
Full textWalker, Jemma. "Bayesian modelling in genetic association studies." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2012. http://researchonline.lshtm.ac.uk/1635516/.
Full textVan, der Laarse Maryn. "Modelling rhino presence with Bayesian networks." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/73455.
Full textDissertation (MEng)--University of Pretoria, 2020.
Industrial and Systems Engineering
MEng (Industrial Engineering)
Unrestricted
Aliverti, Emanuele. "Bayesian modelling of complex dependence structures." Doctoral thesis, Università degli Studi di Padova, 2020. http://hdl.handle.net/10278/3732472.
Full textAliverti, Emanuele. "Bayesian modelling of complex dependence structures." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424720.
Full textStrutture di dipendenza complesse sono molto diffuse in diverse applicazioni. Medicina, biologia, psicologia e scienze sociali sono arricchite da architetture complicate quali reti, tensori e più generalmente dati dipendenti ed ad alta dimensionalità. Strutture di dipendenza articolate stimolano complesse domande di ricerca ed aprono ampi spazi metodologici in diversi ambiti di ricerca statistica, creando una frizzante atmosfera nella quale sviluppare strumenti innovativi. Un obiettivo cruciale nella modellazione statistica di dati complessi consiste nell’estrazione di informazione per condurre inferenza coerente e ottenere risultati affidabili in termini di quantificazione dell’incertezza e di validità per dati futuri. Questi obiettivi necessitano di metodologie statistiche ad-hoc per caratterizzare un modo appropriato le strutture di dipendenza che definiscono dati complessi in quanto tali, migliorando ulteriormente la conoscenza dei meccanismi sottostanti tali strutture. Questa tesi si concentra sulla modellazione Bayesiana di strutture di dipendenza complessa tramite costrutti a variabili latenti. Tale strategia caratterizza la struttura di dipendenza in uno spazio latente, specificando le quantità osservate come condizionatamente indipendenti dato un insieme di attributi latenti, i quali semplificano l’inferenza a posteriori e permettono un’eloquente interpretazione. La tesi è organizzata in tre parti principali, le quali illustrano diverse applicazioni in neuroscienze, psicologia e giustizia criminale. Una modellazione Bayesiana tramite variabili latenti dei dati complessi che nascono in questi ambiti fornisce interessanti intuizioni su diversi aspetti di tali strutture, rispondendo a diverse domande di ricerca e contribuendo alla conoscenza scientifica in materia.
Yu, Qingzhao. "Bayesian synthesis." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155324080.
Full textFrank, Stella Christina. "Bayesian models of syntactic category acquisition." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/6693.
Full textHoulsby, Neil. "Efficient Bayesian active learning and matrix modelling." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/248885.
Full textVieira, Rute Gomes Velosa. "Bayesian phylogenetic modelling of lateral gene transfers." Thesis, University of Newcastle upon Tyne, 2015. http://hdl.handle.net/10443/3018.
Full textCowan, Alexandra. "Modelling trader intentions through evolving Bayesian networks." Thesis, Queen's University Belfast, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725743.
Full textNightingale, Glenna Faith. "Bayesian point process modelling of ecological communities." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/3710.
Full textHearty, Peter Stewart. "Modelling Agile software processes using bayesian networks." Thesis, Queen Mary, University of London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509669.
Full textFord, Oliver P. "Tokamak Plasma Analysis through Bayesian Diagnostic Modelling." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.526369.
Full textAndrade, JoseÌ Ailton Alencar. "Bayesian robustness modelling using regularly varying distributions." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419594.
Full textAguilar, Delil Gomez Portugal. "Bayesian modelling of the radiocarbon calibration curve." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369960.
Full textVermaak, Jaco. "Bayesian modelling and enhancement of speech signals." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621822.
Full textVlasakakis, Georgios. "Application of Bayesian statistics to physiological modelling." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610198.
Full textGroves, Adrian R. "Bayesian learning methods for modelling functional MRI." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:fe46e696-a1a6-4a9d-9dfe-861b05b1ed33.
Full textChen, Younan. "Bayesian hierarchical modelling of dual response surfaces." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/29924.
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Sairam, Nivedita. "Bayesian Approaches for Modelling Flood Damage Processes." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/23083.
Full textFlood damage processes are influenced by the three components of flood risk - hazard, exposure and vulnerability. In comparison to hazard and exposure, the vulnerability component, though equally important is often generalized in many flood risk assessments by a simple depth-damage curve. Hence, this thesis developed a robust statistical method to quantify the role of private precaution in reducing flood vulnerability of households. In Germany, the role of private precaution was found to be very significant in reducing flood damage (11 - 15 thousand euros, per household). Also, flood loss models with structure, parameterization and choice of explanatory variables based on expert knowledge and data-driven methods were successful in capturing changes in vulnerability, which makes them suitable for future risk assessments. Due to significant uncertainty in the underlying data and model assumptions, flood loss models always carry uncertainty around their predictions. This thesis develops Bayesian approaches for flood loss modelling using assumptions regarding damage processes as priors and available empirical data as evidence for updating. Thus, these models provide flood loss predictions as a distribution, that potentially accounts for variability in damage processes and uncertainty in model assumptions. The models presented in this thesis are an improvement over the state-of-the-art flood loss models in terms of prediction capability and model applicability. In particular, the choice of the response (Beta) distribution improved the reliability of loss predictions compared to the popular Gaussian or non-parametric distributions; the Hierarchical Bayesian approach resulted in an improved parameterization of the common stage damage functions that replaces empirical data requirements with region and event-specific expert knowledge, thereby, enhancing its predictive capabilities during spatiotemporal transfer.
Tompkins, Anthony. "Bayesian Spatio-Temporal Modelling with Fourier Features." Thesis, The University of Sydney, 2018. https://hdl.handle.net/2123/21328.
Full textBaker, Jannah F. "Bayesian spatiotemporal modelling of chronic disease outcomes." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104455/1/Jannah_Baker_Thesis.pdf.
Full textCORRADIN, RICCARDO. "Contributions to modelling via Bayesian nonparametric mixtures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241261.
Full textBayesian nonparametric mixtures are flexible models for density estimation and clustering, nowadays a standard tool in the toolbox of applied statisticians. The first proposal of such models was the Dirichlet process (DP) (Ferguson, 1973) mixture of Gaussian kernels by Lo (1984), contribution which paved the way to the definition of a wide variety of nonparametric mixture models. In recent years, increasing interest has been dedicated to the definition of mixture models based on nonparametric mixing measures that go beyond the DP. Among these measures, the Pitman-Yor process (PY) (Perman et al., 1992; Pitman, 1995) and, more in general, the class of Gibbs-type priors (see e.g. De Blasi et al., 2015) stand out for conveniently combining mathematical tractability, interpretability and modelling flexibility. In this thesis we investigate three aspects of nonparametric mixture models, which, in turn, concern their modelling, computational and distributional properties. The thesis is organized as follows. The first chapter proposes a coincise review of the area of Bayesian nonparametric statistics, with focus on tools and models that will be considered in the following chapters. We first introduce the notions of exchangeability, exchangeable partitions and discrete random probability measures. We then focus on the DP and the PY case, main ingredients of second and third chapter, respectively. Finally, we briefly discuss the rationale behind the definition of more general classes of discrete nonparametric priors. In the second chapter we propose a thorough study on the effect of invertible affine transformations of the data on the posterior distribution of DP mixture models, with particular attention to DP mixtures of Gaussian kernels (DPM-G). First, we provide an explicit result relating model parameters and transformations of the data. Second, we formalize the notion of asymptotic robustness of a model under affine transformations of the data and prove an asymptotic result which, by relying on the asymptotic consistency of DPM-G models, show that, under mild assumptions on the data-generating distribution, DPM-G are asymptotically robust. The third chapter presents the ICS, a novel conditional sampling scheme for PY mixture models, based on a useful representation of the posterior distribution of a PY (Pitman, 1996) and on an importance sampling idea, similar in spirit to the augmentation step of the celebrated Algorithm 8 of Neal (2000). The proposed method conveniently combines the best features of state-of-the-art conditional and marginal methods for PY mixture models. Importantly, and unlike its most popular conditional competitors, the numerical efficiency of the ICS is robust to the specification of the parameters of the PY. The steps for implementing the ICS are described in detail and its performance is compared with that one of popular competing algorithms. Finally, the ICS is used as a building block for devising a new efficient algorithm for the class of GM-dependent DP mixture models (Lijoi et al., 2014a; Lijoi et al., 2014b), for partially exchangeable data. In the fourth chapter we study some distributional properties Gibbs-type priors. The main result focuses on an exchangeable sample from a Gibbs-type prior and provides a conveniently simple description of the distribution of the size of the cluster the ( m + 1 ) th observation is assigned to, given an unobserved sample of size m. The study of such distribution provides the tools for a simple, yet useful, strategy for prior elicitation of the parameters of a Gibbs-type prior, in the context of Gibbs-type mixture models. The results in the last three chapters are supported by exhaustive simulation studies and illustrated by analysing astronomical datasets.
Southey, Richard. "Bayesian hierarchical modelling with application in spatial epidemiology." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/59489.
Full textHaasan, Masoud. "Tree-ring growth modelling applied to Bayesian dendrochronology." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/15746/.
Full textLeonte, Daniela School of Mathematics UNSW. "Flexible Bayesian modelling of gamma ray count data." Awarded by:University of New South Wales. School of Mathematics, 2003. http://handle.unsw.edu.au/1959.4/19147.
Full textThomson, Noel. "Bayesian mixture modelling of migration by founder analysis." Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/1468/.
Full textShahtahmassebi, Golnaz. "Bayesian modelling of ultra high-frequency financial data." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/894.
Full textWhitlock, Mark E. "A Bayesian approach to road traffic network modelling." Thesis, University of Kent, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311262.
Full textGOMES, GUILHERME JOSE CUNHA. "MODELLING THE SOIL-ROCK INTERFACE USING BAYESIAN INFERENCE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28488@1.
Full textCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A interface solo-rocha é de difícil determinação e permanece essencialmente desconhecida na maioria das encostas brasileiras. Nesta tese, apresentamos um modelo analítico para a predição espacial da espessura de solo com base na teoria do controle ascendente do maciço rochoso e topografia de alta resolução. A maioria dos parâmetros do modelo possui significado físico, possibilitando medições em campo ou laboratório. O modelo inclui um termo que simula a perda de regolito devido a movimentos de massa estocásticos e outro termo que reproduz a forma do maciço rochoso ao longo de canais de drenagem. Reconciliamos nosso modelo com dados de campo obtidos a partir de sondagens com penetrômetro dinâmico leve no maciço da Tijuca, Rio de Janeiro. Usamos inferência Bayesiana, com amostragem da distribuição posterior de parâmetros através de simulação Monte Carlo via cadeia de Markov, a qual forneceu parâmetros do modelo que melhor honram os dados de campo bem como a incerteza preditiva estratigráfica. Para testar os resultados da inferência Bayesiana em estabilidade de encostas, desenvolvemos um programa computacional para a integração de simulações de fluxo não-saturado, o qual proporciona a distribuição de poro pressões, e um código de análise limite numérica, que fornece o fator de segurança (FS), ambos em três-dimensões. Propagamos a incerteza estratigráfica no programa desenvolvido para quantificar a variabilidade do FS e a probabilidade de ruptura de uma encosta natural não-saturada existente na região de estudo. Finalmente, salientamos a importância da quantificação da topografia da interface solo-rocha em análises de estabilidade geotécnica.
Soil-bedrock interface is difficult to determine and remains essentially unknown in most Brazilian slopes. In this thesis, we present an analytic model for the spatial prediction of regolith depth built on the bottomup control on fresh bedrock topography hypothesis and high-resolution topographic data. Most of the parameters of the model represent physical entities that can be measured directly in the laboratory or field. The model includes a term which simulates the loss of regolith due to stochastic mass movements and another term that mimic the bedrock-valley morphology. We reconcile our model with field observations from boreholes using a light dynamic penetrometer at Tijuca massif, Rio de Janeiro. We use Bayesian inference, with Markov chain Monte Carlo simulation to summarize the posterior distribution of the parameters, which led to model parameters that best honor our field data as well as the stratigraphic predictive uncertainty. To test the results of the Bayesian inference in slope stability, we develop a software to integrate unsaturated flow simulations, which provide the pressure head distributions and a numerical limit analysis code, that generates the factor of safety (FS), both in three dimensions. We propagate the stratigraphic uncertainty through the developed program to quantify the FS variability and the probability of failure of a natural unsaturated hillslope in the study region. Finally, we emphasize the importance of bedrock topography in slope stability analysis.
Loza, Reyes Elisa. "Classification of phylogenetic data via Bayesian mixture modelling." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.
Full textConsul, Juliana Iworikumo. "Flexible Bayesian modelling of covariate effects on survival." Thesis, University of Newcastle upon Tyne, 2016. http://hdl.handle.net/10443/3535.
Full textLi, Xuguang. "Modelling financial volatility using Bayesian and conventional methods." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/82685/.
Full textZhang, Yan. "Variational Bayesian data driven modelling for biomedical systems." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/89458/.
Full textDuncan, Earl W. "Bayesian approaches to issues arising in spatial modelling." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112356/1/Earl_Duncan_Thesis.pdf.
Full textBleki, Zolisa. "Efficient Bayesian analysis of spatial occupancy models." Master's thesis, University of Cape Town, 2020. http://hdl.handle.net/11427/32469.
Full textCaballero, Jose Louis Galan. "Modeling qualitative judgements in Bayesian networks." Thesis, Queen Mary, University of London, 2008. http://qmro.qmul.ac.uk/xmlui/handle/123456789/28170.
Full textMavros, George. "Bayesian stochastic mortality modelling under serially correlated local effects." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/2917.
Full textNewman, Keith. "Bayesian modelling of latent Gaussian models featuring variable selection." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3700.
Full textDou, Yiping. "Dynamic Bayesian models for modelling environmental space-time fields." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/634.
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