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Rozprawy doktorskie na temat „Bayesian modelling”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Peeling, Paul Halliday. "Bayesian methods in music modelling." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/237236.

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This thesis presents several hierarchical generative Bayesian models of musical signals designed to improve the accuracy of existing multiple pitch detection systems and other musical signal processing applications whilst remaining feasible for real-time computation. At the lowest level the signal is modelled as a set of overlapping sinusoidal basis functions. The parameters of these basis functions are built into a prior framework based on principles known from musical theory and the physics of musical instruments. The model of a musical note optionally includes phenomena such as frequency an

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Strimenopoulou, Foteini. "Bayesian modelling of functional data." Thesis, University of Kent, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544037.

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Polson, Nicholas G. "Bayesian perspectives on statistical modelling." Thesis, University of Nottingham, 1988. http://eprints.nottingham.ac.uk/11292/.

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This thesis explores the representation of probability measures in a coherent Bayesian modelling framework, together with the ensuing characterisation properties of posterior functionals. First, a decision theoretic approach is adopted to provide a unified modelling criterion applicable to assessing prior-likelihood combinations, design matrices, model dimensionality and choice of sample size. The utility structure and associated Bayes risk induces a distance measure, introducing concepts from differential geometry to aid in the interpretation of modelling characteristics. Secondly, analytical

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Baker, Peter John. "Applied Bayesian modelling in genetics." Thesis, Queensland University of Technology, 2001.

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Habli, Nada. "Nonparametric Bayesian Modelling in Machine Learning." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34267.

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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 by Ferguson (1983), its conjugacy property allows a tractable posterior inference which has lately given rise to a significant developments in applications related to machine learning. Another yet widespread prior used in nonparametric Bayesian inference is the Beta process and its extensions. It has

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6

Delatola, Eleni-Ioanna. "Bayesian nonparametric modelling of financial data." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589934.

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This thesis presents a class of discrete time univariate stochastic volatility models using Bayesian nonparametric techniques. In particular, the models that will be introduced are not only the basic stochastic volatility model, but also the heavy-tailed model using scale mixture of Normals and the leverage model. The aim will be focused on capturing flexibly the distribution of the logarithm of the squared return under the aforementioned models using infinite mixture of Normals. Parameter estimates for these models will be obtained using Markov chain Monte Carlo methods and the Kalman filter.

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Yan, Haojie. "Bayesian spatial modelling of air pollution." Thesis, University of Bath, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541668.

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8

Brown, G. O. "Model discrimination in Bayesian credibility modelling." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596996.

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This thesis is about insurance models and aspects of uncertainty pertaining to such models. The models we consider are insurance credibility models, arising from the need for accurate rate making based on past experience of claims in some portfolio of insurance policies. Classical credibility modelling is concerned with the use of a linear estimate to approximate the risk premium and was first studied by American actuaries at the start of the 20<sup>th</sup> century. In the Bayesian paradigm the credibility premium is the optimal linear premium since it minimises the expected square loss based

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9

Kheradmandnia, Manouchehr. "Aspects of Bayesian threshold autoregressive modelling." Thesis, University of Kent, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303040.

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10

Smith, 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.

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11

Chai, High Seng. "Bayesian modelling with skew-elliptical distributions." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432726.

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Walker, 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/.

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Bayesian Model Selection Approaches are flexible methods that can be utilised to investigate Genetic Association studies in greater detail; enabling us to more accurately pin-point locations of disease genes in complex regions such as the MHC, as well as investigate possible causal pathways between genes, disease and intermediate phenotypes. This thesis is split into two distinct parts. The first uses a Bayesian Multivariate Adaptive Regression Spline Model to search across many highly correlated variants to try to determine which are likely to be the truly causal variants within complex genet

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Van, der Laarse Maryn. "Modelling rhino presence with Bayesian networks." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/73455.

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Modelling complex systems such as how the white rhinoceros Ceratotherium simum simum uses a landscape requires innovative and multi-disciplinary approaches. Bayesian networks have been shown to provide a dynamic, easily interpretable framework to represent real-world problems. This, together with advances in remote sensor technology to easily quantify environmental variables, make non-intrusive techniques for understanding and inference of ecological processes more viable than ever. However, when modelling an animal’s use of a landscape we only have access to presence locations. These data ar

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14

Aliverti, Emanuele. "Bayesian modelling of complex dependence structures." Doctoral thesis, Università degli Studi di Padova, 2020. http://hdl.handle.net/10278/3732472.

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Aliverti, Emanuele. "Bayesian modelling of complex dependence structures." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424720.

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Complex dependence structures characterising modern data are routinely encountered in a large variety of research fields. Medicine, biology, psychology and social sciences are enriched by intricate architectures such as networks, tensors and more generally high-dimensional dependent data. Rich dependence structures stimulate challenging research questions and open wide methodological avenues in different areas of statistical research, providing an exciting atmosphere to develop innovative tools. A primary interest in statistical modelling of complex data is on adequately extracting infor

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16

Yu, Qingzhao. "Bayesian synthesis." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155324080.

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17

Frank, Stella Christina. "Bayesian models of syntactic category acquisition." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/6693.

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Discovering a word’s part of speech is an essential step in acquiring the grammar of a language. In this thesis we examine a variety of computational Bayesian models that use linguistic input available to children, in the form of transcribed child directed speech, to learn part of speech categories. Part of speech categories are characterised by contextual (distributional/syntactic) and word-internal (morphological) similarity. In this thesis, we assume language learners will be aware of these types of cues, and investigate exactly how they can make use of them. Firstly, we enrich the context

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18

Houlsby, Neil. "Efficient Bayesian active learning and matrix modelling." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/248885.

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With the advent of the Internet and growth of storage capabilities, large collections of unlabelled data are now available. However, collecting supervised labels can be costly. Active learning addresses this by selecting, sequentially, only the most useful data in light of the information collected so far. The online nature of such algorithms often necessitates efficient computations. Thus, we present a framework for information theoretic Bayesian active learning, named Bayesian Active Learning by Disagreement, that permits efficient and accurate computations of data utility. Using this framew

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Vieira, Rute Gomes Velosa. "Bayesian phylogenetic modelling of lateral gene transfers." Thesis, University of Newcastle upon Tyne, 2015. http://hdl.handle.net/10443/3018.

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Phylogenetic trees represent the evolutionary relationships between a set of species. Inferring these trees from data is particularly challenging sometimes since the transfer of genetic material can occur not only from parents to their o spring but also between organisms via lateral gene transfers (LGTs). Thus, the presence of LGTs means that genes in a genome can each have di erent evolutionary histories, represented by di erent gene trees. A few statistical approaches have been introduced to explore non-vertical evolution through collections of Markov-dependent gene trees. In 2005 Suchard de

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Cowan, 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.

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This research highlights the problem of trade based market manipulation in financial markets, where an individual or party aim to distort the pricing mechanism and gain profit at the expense of law abiding investors. This research evaluates data mining approaches applied to financial market surveillance and addresses a current deficit in literature with regards to modelling traders at an entity level. A system is proposed, named the Evolving Bayesian Network (EBN), to model an individual trader's behaviour using transaction order data generated by the participant. The aim of the model is to in

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21

Nightingale, Glenna Faith. "Bayesian point process modelling of ecological communities." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/3710.

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The modelling of biological communities is important to further the understanding of species coexistence and the mechanisms involved in maintaining biodiversity. This involves considering not only interactions between individual biological organisms, but also the incorporation of covariate information, if available, in the modelling process. This thesis explores the use of point processes to model interactions in bivariate point patterns within a Bayesian framework, and, where applicable, in conjunction with covariate data. Specifically, we distinguish between symmetric and asymmetric species

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22

Hearty, 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.

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23

Ford, 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.

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24

Andrade, 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.

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25

Aguilar, 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.

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Vermaak, Jaco. "Bayesian modelling and enhancement of speech signals." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621822.

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27

Vlasakakis, Georgios. "Application of Bayesian statistics to physiological modelling." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610198.

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28

Groves, 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.

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Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permitting probabilistic inference on hierarchical, generative models of data. This thesis primarily develops Bayesian analysis techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function, perfusion, and structure in the human brain. The first part of this work fits nonlinear biophysical models to multimodal functional MRI data within a variational Bayes framework. Simultaneously-acquired multimodal data contains mixtures of different signals and ther

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29

Chen, Younan. "Bayesian hierarchical modelling of dual response surfaces." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/29924.

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Dual response surface methodology (Vining and Myers (1990)) has been successfully used as a cost-effective approach to improve the quality of products and processes since Taguchi (Tauchi (1985)) introduced the idea of robust parameter design on the quality improvement in the United States in mid-1980s. The original procedure is to use the mean and the standard deviation of the characteristic to form a dual response system in linear model structure, and to estimate the model coefficients using least squares methods. In this dissertation, a Bayesian hierarchical approach is proposed to model th

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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.

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Hochwasserschadensprozesse werden von den drei Komponenten des Hochwasserrisikos bestimmt – der Gefahr, der Exposition und der Vulnerabilität. Dabei bleiben wichtige Einflussgrößen auf die Vulnerabilität, wie die private Hochwasservorsorge aufgrund fehlender quantitativer Informationen unberücksichtigt. Diese Arbeit entwickelt daher eine robuste statistische Methode zur Quantifizierung des Einflusses von privater Hochwasservorsorge auf die Reduzierung der Vulnerabilität von Haushalten bei Hochwasser. Es konnte gezeigt werden, dass in Deutschland private Hochwasservorsorgemaßnahmen den durchsch

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31

Tompkins, Anthony. "Bayesian Spatio-Temporal Modelling with Fourier Features." Thesis, The University of Sydney, 2018. https://hdl.handle.net/2123/21328.

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One of the most powerful machine learning techniques is \emph{Gaussian Processes} (GPs) which incur an $O(N^3)$ complexity in the number of data samples. In regression and classification there exist approximation methods which typically rely on $M$ \emph{inducing points} but still typically incur an $O(NM^2)$ complexity in the data and corresponding inducing points which have reduced expressiveness the larger the dataset becomes. These methods are typically unable to learn if the number of datapoints becomes computationally intractable. It is this limitation of traditional methods that invites

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Baker, 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.

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This thesis contributes to Bayesian spatial and spatiotemporal methodology by investigating techniques for spatial imputation and joint disease modelling, and identifies high-risk individual profiles and geographic areas for type II diabetes mellitus (DMII) outcomes. DMII and related chronic conditions including hypertension, coronary arterial disease, congestive heart failure and chronic obstructive pulmonary disease are examples of ambulatory care sensitive conditions for which hospitalisation for complications is potentially avoidable with quality primary care. Bayesian spatial and spatiote

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CORRADIN, RICCARDO. "Contributions to modelling via Bayesian nonparametric mixtures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241261.

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I modelli mistura in ambito Bayesiano nonparametrico sono modelli flessibili per stime di densità e clustering, ormai uno strumento di uso comune in ambito statistico applicato. Il primo modello introdotto in questo ambito è stato il processo di Dirichlet (DP) (Ferguson, 1973) combinato con un kernel Gaussiano(Lo, 1984). Recentemente è cresciuto l’interesse verso la definizione di modelli mistura basati su misure nonparametriche che generalizzano il DP. Tra le misure proposte, il processo di Pitman-Yor (PY) (Perman et al., 1992; Pitman, 1995) e, più in generale, la classe di Gibbs-type prior (

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34

Southey, Richard. "Bayesian hierarchical modelling with application in spatial epidemiology." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/59489.

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Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrel

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Haasan, Masoud. "Tree-ring growth modelling applied to Bayesian dendrochronology." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/15746/.

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Classical dendrochronology involves using standard statistical methods, such as correlation coefficients and t-values to crossmatch undated tree-ring width sequences to dated 'master' chronologies. This crossmatching process aims to identify the 'best' offset between the dated and undated sequences with a view to providing a calendar date estimate for the undated trees. Motivated by the successful and routine use of Bayesian statistical methods to provide a fully probabilistic approach to radiocarbon dating, this thesis investigates the practicality of using a process-based forward model known

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Leonte, 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.

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Bayesian approaches to prediction and the assessment of predictive uncertainty in generalized linear models are often based on averaging predictions over different models, and this requires methods for accounting for model uncertainty. In this thesis we describe computational methods for Bayesian inference and model selection for generalized linear models, which improve on existing techniques. These methods are applied to the building of flexible models for gamma ray count data (data measuring the natural radioactivity of rocks) at the Castlereagh Waste Management Centre, which served as a ha

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37

Thomson, Noel. "Bayesian mixture modelling of migration by founder analysis." Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/1468/.

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In this thesis a new method is proposed to estimate major periods of migration from one region into another using phased, non-recombined sequence data from the present. The assumption is made that migration occurs in multiple waves and that during each migration period, a number of sequences, called `founder sequences', migrate into the new region. It is first shown through appropriate simulations based on the structured coalescent that previous inferences based on the idea of founder sequences sufer from the fundamental problem that it is assumed that migration events coincide with the nodes

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Shahtahmassebi, Golnaz. "Bayesian modelling of ultra high-frequency financial data." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/894.

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The availability of ultra high-frequency (UHF) data on transactions has revolutionised data processing and statistical modelling techniques in finance. The unique characteristics of such data, e.g. discrete structure of price change, unequally spaced time intervals and multiple transactions have introduced new theoretical and computational challenges. In this study, we develop a Bayesian framework for modelling integer-valued variables to capture the fundamental properties of price change. We propose the application of the zero inflated Poisson difference (ZPD) distribution for modelling UHF d

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Whitlock, 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.

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GOMES, 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.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>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 devid

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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.

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Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such a simplistic, homogeneous formulation may be a poor description of reality when the data arise from heterogeneous processes. A well-known example is when sites evolve at heterogeneous rates. This thesis is a contribution to the modelling and understanding of heterogeneityin phylogenetic data. Weproposea methodfor the classificationof DN

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Consul, Juliana Iworikumo. "Flexible Bayesian modelling of covariate effects on survival." Thesis, University of Newcastle upon Tyne, 2016. http://hdl.handle.net/10443/3535.

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Proportional hazards models are commonly used in survival analysis. Typically a baseline hazard function is combined with hazard multipliers which depend on covariate values through a logarithmic link function and a linear predictor. Models have been developed which allow exibility in the form of the baseline hazard. However, the form of dependence of the hazard multipliers on covariates is usually speci ed. The aim of this research is to introduce exibility into the form of the dependence of the hazard function on the covariates by removing the assumptions of parametric forms which are usuall

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Li, Xuguang. "Modelling financial volatility using Bayesian and conventional methods." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/82685/.

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This thesis investigates different volatility measures and models, including parametric and non-parametric volatility measurement. Both conventional and Bayesian methods are used to estimate volatility models. Chapter 1: We model and forecast intraday return volatility based on an extended stochastic volatility (SV) specification. Compared with the standard SV, we incorporate the trading duration information which includes both actual and expected durations. We use the Autoregressive Conditional Duration (ACD) model to calculate the expected duration that can be used to measure the surprise in

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44

Zhang, Yan. "Variational Bayesian data driven modelling for biomedical systems." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/89458/.

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Physiological systems are well recognised to be nonlinear, stochastic and complex. In situations when only one time series of a single variable is available, exacting useful information from the dynamic data is crucial to facilitate personalised clinical decisions and deepen the understanding of the underlying mechanisms. This thesis is focused on establishing and validating data-driven models that incorporate nonlinearity and stochasticity into the model developing framework, to describe a single measurement time series in the field of biomedical engineering. The tasks of model selection and

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45

Duncan, 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.

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This thesis addressed several contemporary issues arising in the analysis of spatial data and the broader statistical methodology. Two state-of-the-art statistical models are developed for the purpose of identifying unusual trends, a new algorithm to deal with label switching is devised which outperforms existing solutions, and new approaches to spatial smoothing are explored. The outcomes from this thesis should be of interest to managers in the health sector, biostatisticians, and researchers who deal with spatial data.

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Bleki, Zolisa. "Efficient Bayesian analysis of spatial occupancy models." Master's thesis, University of Cape Town, 2020. http://hdl.handle.net/11427/32469.

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Species conservation initiatives play an important role in ecological studies. Occupancy models have been a useful tool for ecologists to make inference about species distribution and occurrence. Bayesian methodology is a popular framework used to model the relationship between species and environmental variables. In this dissertation we develop a Gibbs sampling method using a logit link function in order to model posterior parameters of the single-season spatial occupancy model. We incorporate the widely used Intrinsic Conditional Autoregressive (ICAR) prior model to specify the spatial rando

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47

Caballero, 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.

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Although Bayesian Networks (BNs) are increasingly being used to solve real world problems [47], their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. This thesis describes an approach to defining NPTs for a large class of commonly occurring nodes called ranked nodes. This approach is based on the doubly truncated Normal distribution with a central tendency that is

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48

Mavros, George. "Bayesian stochastic mortality modelling under serially correlated local effects." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/2917.

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The vast majority of stochastic mortality models in the academic literature are intended to explain the dynamics underpinning the process by a combination of age, period and cohort e ects. In principle, the more such e ects are included in a stochastic mortality model, the better is the in-sample t to the data. Estimates of those parameters are most usually obtained under some distributional assumption about the occurrence of deaths, which leads to the optimisation of a relevant objective function. The present Thesis develops an alternative framework where the local mortality effect is appreci

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Newman, Keith. "Bayesian modelling of latent Gaussian models featuring variable selection." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3700.

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Latent Gaussian models are popular and versatile models for performing Bayesian inference. In many cases, these models will be analytically intractable creating a need for alternative inference methods. Integrated nested Laplace approximations (INLA) provides fast, deterministic inference of approximate posterior densities by exploiting sparsity in the latent structure of the model. Markov chain Monte Carlo (MCMC) is often used for Bayesian inference by sampling from a target posterior distribution. This suffers poor mixing when many variables are correlated, but careful reparameterisation or

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Dou, 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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This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to i

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