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1
Ling, Yuheng. "Corsican housing market analysis : Applications of bayesian hierarchical model." Thesis, Corte, 2020. http://www.theses.fr/2020CORT0011.
Повний текст джерелаАнотація:
Ce travail de thèse porte sur le développement de modèles économétriques/statistiques spatiaux pour analyser le marché immobilier en Corse. Concernant les contributions techniques, j'aborde dans ce travail la question de l'autocorrélation spatiale et temporelle dans le résidu de la régression linéaire classique qui peut conduire à des estimations biaisées. Les premières études empiriques utilisant des outils « a-spatiaux », tels que la méthode des moindres carrés ordinaires, ont ainsi probablement produit des estimations biaisées. Grâce à l’adoption de techniques basées sur l'économétrie spatiale, les économistes peuvent désormais gérer de manière plus efficace les problèmes liés à la présence d'autocorrélations dans les données. Cependant, la prise en compte de la dimension temporelle dans ce type de modèles demeure « floue » en raison du recours à des paramètres complexes qu’elle nécessite. Pour faire face à l'autocorrelation spatiale et temporelle, j’ai eu recours à l'application de modèles spatiotemporels hiérarchiques bayésiens. En termes d'économie régionale, j’ai utilisé les modèles hiérarchiques spatiotemporels bayésiens que j’ai développés pour évaluer le marché immobilier en Corse. En particulier, la question de savoir en quoi l’emplacement géographique affecte les caractéristiques du logement (prix, destination principale) constitue le cœur de cette thèse. Les sujets analysés sont complexes car ils traitent de questions allant de la prévision des prix de vente des appartements en Corse, à l'enquête sur les taux des résidences secondaires et à l'évaluation de l'impact de la vue sur mer. En outre, les fondements économiques de ces thématiques reposent sur la méthode des prix hédoniques, la prise en compte d’effets adjacents (adjacent effects) et d’effets d’entrainement (ripple effects). Enfin, j'identifie les points chauds (hot spots) et les points froids (cold spots) en termes de prix des appartements et de taux des résidences secondaires, et j’évalue l’impact de la vue sur mer (la mer Méditerranée dans le cadre de ce travail) et de l'accessibilité à la côte sur les prix des appartements. Ces résultats devraient fournir de précieuses informations pouvant aider à la prise de décision des planificateurs en matière d’urbanisation et des décideurs publicsThis thesis focuses on the development of spatial econometric/statistical models that are used for analyzing the Corsican real estate market.Concerning technical contributions, I address the issue of spatial and temporal autocorrelation in the residual of classical linear regression that may yield biased estimates. Early empirical studies using “spaceless” tools such as OLS probably yield biased estimates. With the acceptance of spatial econometrics, regional scientists can better handle the autocorrelation in data. However, the temporal dimension remains unclear due to its complex settings. To tackle both spatial and temporal autocorrelation, I suggest applying Bayesian hierarchical spatiotemporal models.Regarding the contribution in terms of regional economics, the developed ad-hoc Bayesian spatiotemporal hierarchical models have been used to assess the Corsican housing market. In particular, how locations affect housing is the key issue in this thesis. The topics analyzed are complex because they deal with issues ranging from predicting Corsican apartment sales prices, investigating second home rates to assessing the impact of sea views. Furthermore, the economic underpinnings of these topics include the hedonic price method, the adjacent effects and the ripple effects.Finally, I identify “hot spots” and “cold spots” in terms of apartment prices and second home rates, and I also indicate that both the sea (Mediterranean Sea) view and the coast accessibility affect apartment prices. These findings should provide valuable information for planners and policymakers
2
Al-Kaabawi, Zainab A. A. "Bayesian hierarchical models for linear networks." Thesis, University of Plymouth, 2018. http://hdl.handle.net/10026.1/12829.
Повний текст джерелаАнотація:
A motorway network is handled as a linear network. The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two mechanisms have been adopted to achieve this aim. The first, the motorway-specific intensity is estimated by modelling the point pattern of the accident data using a homogeneous Poisson process. The homogeneous Poisson process is used to model all intensities but heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second mechanism, the segment-specific intensity is estimated by modelling the point pattern of the accident data. The homogeneous Poisson process is used to model accident data within segments but heterogeneity across segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo simulation algorithms is used in order to estimate the unknown parameters in the models and a sensitivity analysis to the prior choice is assessed. The performance of the proposed models is checked through a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The performance of the three-level frequentist model was poor. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between the two-level Bayesian hierarchical model and the three-level Bayesian hierarchical model, where the results showed that the best fitting model was the three-level Bayesian hierarchical model.
3
Woodard, Roger. "Bayesian hierarchical models for hunting success rates /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9951135.
Повний текст джерела
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Wang, Xiaogang Ph D. Massachusetts Institute of Technology. "Learning motion patterns using hierarchical Bayesian models." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53306.
Повний текст джерелаАнотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 163-179).
In far-field visual surveillance, one of the key tasks is to monitor activities in the scene. Through learning motion patterns of objects, computers can help people understand typical activities, detect abnormal activities, and learn the models of semantically meaningful scene structures, such as paths commonly taken by objects. In medical imaging, some issues similar to learning motion patterns arise. Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is one of the first methods to visualize and quantify the organization of white matter in the brain in vivo. Using methods of tractography segmentation, one can connect local diffusion measurements to create global fiber trajectories, which can then be clustered into anatomically meaningful bundles. This is similar to clustering trajectories of objects in visual surveillance. In this thesis, we develop several unsupervised frameworks to learn motion patterns from complicated and large scale data sets using hierarchical Bayesian models. We explore their applications to activity analysis in far-field visual surveillance and tractography segmentation in medical imaging. Many existing activity analysis approaches in visual surveillance are ad hoc, relying on predefined rules or simple probabilistic models, which prohibits them from modeling complicated activities. Our hierarchical Bayesian models can structure dependency among a large number of variables to model complicated activities. Various constraints and knowledge can be nicely added into a Bayesian framework as priors. When the number of clusters is not well defined in advance, our nonparametric Bayesian models can learn it driven by data with Dirichlet Processes priors.
(cont.) In this work, several hierarchical Bayesian models are proposed considering different types of scenes and different settings of cameras. If the scenes are crowded, it is difficult to track objects because of frequent occlusions and difficult to separate different types of co-occurring activities. We jointly model simple activities and complicated global behaviors at different hierarchical levels directly from moving pixels without tracking objects. If the scene is sparse and there is only a single camera view, we first track objects and then cluster trajectories into different activity categories. In the meanwhile, we learn the models of paths commonly taken by objects. Under the Bayesian framework, using the models of activities learned from historical data as priors, the models of activities can be dynamically updated over time. When multiple camera views are used to monitor a large area, by adding a smoothness constraint as a prior, our hierarchical Bayesian model clusters trajectories in multiple camera views without tracking objects across camera views. The topology of multiple camera views is assumed to be unknown and arbitrary. In tractography segmentation, our approach can cluster much larger scale data sets than existing approaches and automatically learn the number of bundles from data. We demonstrate the effectiveness of our approaches on multiple visual surveillance and medical imaging data sets.
by Xiaogang Wang.
Ph.D.
5
Jaberansari, Negar. "Bayesian Hierarchical Models for Partially Observed Data." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479818516727153.
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Lu, Jun. "Bayesian hierarchical models and applications in psychology research /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144437.
Повний текст джерела
7
Lin, Xiaoyan. "Bayesian hierarchical models for the recognition-memory experiments." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6047.
Повний текст джерелаАнотація:
Thesis (Ph. D.)--University of Missouri-Columbia, 2008.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.
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Bloomquist, Erik William. "Bayesian hierarchical models to untangle complex evolutionary histories." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1971755201&sid=35&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Повний текст джерела
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Israeli, Yeshayahu D. "Whitney Element Based Priors for Hierarchical Bayesian Models." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1621866603265673.
Повний текст джерела
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Krachey, Matthew James. "Hierarchical Bayesian application to instantaneous rates tag-return models." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-08182009-100250/.
Повний текст джерелаАнотація:
Natural mortality has always been a challenging quantity to estimate in harvested populations. The most common approaches to estimation include a regression model based on life history parameters and more recently tag-return models. In recent years, Bayesian methods have been increasingly implemented in ecological models due to their ability to handle increased model complexity and auxiliary datasets. In this dissertation, I explore the implementation of Bayesian methods to analyze tag-return data focusing on natural mortality. Chapter 1 is focused on the addition of two components to the tag-return model framework: random effects and auxiliary data. Auxiliary information on the instantaneous rate of natural mortality is provided through Hoenig's equation relating lifespan to natural mortality, and also implemented through a hierarchical prior. A simulation study validates the performance of the model while an analysis of the classic Cayuga Lake trout dataset demonstrates its use. Chapter 2 adds a change-point allowing for the estimation of two levels of natural mortality and the timing of the discrete-time shift in mortality. Analysis is focused on a Chesapeake Bay striped bass tagging dataset of fish tagged at six years of age and older from 1991-2002. Results show the ability to account for shift in timing. Contrasting with Jiang et al.'s study on the same striped bass dataset, the timing of the change-point was different between the two studies, likely because the Jiang study assumed a fixed tag-reporting probability of 0.43 whereas estimates seem to indicate it may be closer to 0.3. Chapter 3 introduces a change-point allowing for a shift in the tag-reporting probability while assuming a constant natural mortality rate. High reward tags are included in a subset of the data time-series to improve estimation. A factorial simulation design was used to investigate the model performance with different reporting rate and high reward tag scenarios. In general, the model performed very well with little bias except in the case of no high-reward tags. The model performed surprisingly well in a six year study. The results suggest the importance of high reporting rates and/ or auxiliary data sources such as high reward tags.
11
Frühwirth-Schnatter, Sylvia, and Regina Tüchler. "Bayesian parsimonious covariance estimation for hierarchical linear mixed models." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/774/1/document.pdf.
Повний текст джерелаАнотація:
We considered a non-centered parameterization of the standard random-effects model, which is based on the Cholesky decomposition of the variance-covariance matrix. The regression type structure of the non-centered parameterization allows to choose a simple, conditionally conjugate normal prior on the Cholesky factor. Based on the non-centered parameterization, we search for a parsimonious variance-covariance matrix by identifying the non-zero elements of the Cholesky factors using Bayesian variable selection methods. With this method we are able to learn from the data for each effect, whether it is random or not, and whether covariances among random effects are zero or not. An application in marketing shows a substantial reduction of the number of free elements of the variance-covariance matrix. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematics
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Ulrich, Michael David. "Meta-Analysis Using Bayesian Hierarchical Models in Organizational Behavior." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2349.
Повний текст джерелаАнотація:
Meta-analysis is a tool used to combine the results from multiple studies into one comprehensive analysis. First developed in the 1970s, meta-analysis is a major statistical method in academic, medical, business, and industrial research. There are three traditional ways in which a meta-analysis is conducted: fixed or random effects, and using an empirical Bayesian approach. Derivations for conducting meta-analysis on correlations in the industrial psychology and organizational behavior (OB) discipline were reviewed by Hunter and Schmidt (2004). In this approach, Hunter and Schmidt propose an empirical Bayesian analysis where the results from previous studies are used as a prior. This approach is still widely used in OB despite recent advances in Bayesian methodology. This paper presents the results of a hierarchical Bayesian model for conducting meta-analysis of correlations and then compares these results to a traditional Hunter-Schmidt analysis conducted by Judge et al. (2001). In our approach we treat the correlations from previous studies as a likelihood, and present a prior distribution for correlations.
13
Meager, Rachael. "Evidence aggregation in development economics via Bayesian hierarchical models." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111358.
Повний текст джерелаАнотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 185-193).
It is increasingly recognized that translating research into policy requires aggregating evidence from multiple studies of the same economic phenomenon. This translation requires not only an estimate of the impact of an intervention across different contexts, but also an assessment of the generalizability of the evidence and hence its applicability to policy decisions in other settings. This thesis performs evidence aggregation using Bayesian hierarchical models, which both aggregate evidence and assess the true underlying heterogeneity across settings, for applications in development economics. Where necessary, the thesis develops new methods to aggregate evidence on certain measures of evidence currently neglected in the aggregation literature such as distributional treatment effects or risk ratios. The applications considered are randomized controlled trials of expanding access to microcredit and randomized access to vitamin A supplementation in developing nations.
by Rachael Meager.
Ph. D.
14
Pflugeisen, Bethann Mangel. "Analysis of Otolith Microchemistry Using Bayesian Hierarchical Mixture Models." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275059376.
Повний текст джерела
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Karseras, Evripidis. "Hierarchical Bayesian models for sparse signal recovery and sampling." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32102.
Повний текст джерелаАнотація:
This thesis builds upon the problem of sparse signal recovery from the Bayesian standpoint. The advantages of employing Bayesian models are underscored, with the most important being the ease at which a model can be expanded or altered; leading to a fresh class of algorithms. The thesis fills out several gaps between sparse recovery algorithms and sparse Bayesian models; firstly the lack of global performance guarantees for the latter and secondly what the signifying differences are between the two. These questions are answered by providing; a refined theoretical analysis and a new class of algorithms that combines the benefits from classic recovery algorithms and sparse Bayesian modelling. The said Bayesian techniques find application in tracking dynamic sparse signals, something impossible under the Kalman filter approach. Another innovation of this thesis are Bayesian models for signals whose components are known a priori to exhibit a certain statistical trend. These situations require that the model enforces a given statistical bias on the solutions. Existing Bayesian models can cope with this input, but the algorithms to carry out the task are computationally expensive. Several ways are proposed to remedy the associated problems while still attaining some form of optimality. The proposed framework finds application in multipath channel estimation with some very promising results. Not far from the same area lies that of Approximate Message Passing. This includes extremely low-complexity algorithms for sparse recovery with a powerful analysis framework. Some results are derived, regarding the differences between these approximate methods and the aforementioned models. This can be seen as preliminary work for future research. Finally, the thesis presents a hardware implementation of a wideband spectrum analyser based on sparse recovery methods. The hardware consists of a Field-Programmable Gate Array coupled with an Analogue to Digital Converter. Some critical results are drawn, regarding the gains and viability of such methods.
16
Butler, Allison M. "Hierarchical Probit Models for Ordinal Ratings Data." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2656.
Повний текст джерелаАнотація:
University students often complete evaluations of their courses and instructors. The evaluation tool typically contains questions about the course and the instructor on an ordinal Likert scale. We assess instructor effectiveness while adjusting for known confounders. We present a probit regression model with a latent variable to measure the instructor effectiveness accounting for student specific covariates, such as student grade in the course, high school and university GPA, and ACT score.
17
Kim, Yong Ku. "Bayesian multiresolution dynamic models." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180465799.
Повний текст джерела
18
Thériault, Marc-Erick. "Bayesian hierarchical models for mapping lung cancer mortality in Ontario." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ53358.pdf.
Повний текст джерела
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Al-Jaralla, Reem Abdulla. "Optimal design for Bayesian linear hierarchical models with measurement error." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248202.
Повний текст джерела
20
Yalamanchili, Pavan Kumar. "Hierarchical Bayesian cortical models analysis and acceleration on multicore architectures /." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252937873/.
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Bass, Mark. "Efficient parameterisation of hierarchical Bayesian models for spatially correlated data." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/385240/.
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Li, Qie. "A Bayesian Hierarchical Model for Multiple Comparisons in Mixed Models." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1342530994.
Повний текст джерела
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Tunaru, Radu. "Statistical modelling of road accident data via graphical models and hierarchical Bayesian models." Thesis, Middlesex University, 1999. http://eprints.mdx.ac.uk/8030/.
Повний текст джерелаАнотація:
The objective of this thesis is to develop statistical models for multivariate road accident data. Two directions of research are followed: graphical modelling for contingency tables cross-classified by accident characteristics, and hierarchical Bayesian models for multiple accident frequencies of different types modelled jointly. Multi-dimensional tables are analysed and it is shown how to use collapsibility to reduce the dimensionality of the analysis without the problems of Simpson's paradox. It is revealed that accident severity and the number of casualties are associated, and that these variables are mainly influenced by the number of vehicles and speed limit. Graphical chain models allow causal hypotheses to be formulated and it is shown how they are valuable tools for empirical research about road accident characteristics. The hierarchical Bayesian models developed combine generalized linear models with random effects. The novelty of these models consists in the joint modelling of multiple response variables. The models account for overdispersion and they are used for accident prediction and for ranking hazardous sites. All models are fully Bayesian and are fitted using Markov Chain Monte Carlo methods. It is shown that multiple response variables models are superior to separate univariate response models. Some theoretical problems are examined regarding the maximum likelihood estimation process for the two parameters negative binomial distribution. A condition is given that is equivalent with unique maximum likelihood estimators. The two directions of research are connected by using graphs to describe the models. In addition, a new Bayesian model selection procedure for contingency tables is proposed. This is based on Gibbs sampling and avoids problems associated with asymptotic tests. The conclusions revealed here can help practitioners to design better safety policies and to spend money more wisely on sites that really are dangerous.
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Ayala, Christian A. "Acceptance-Rejection Sampling with Hierarchical Models." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/cmc_theses/1162.
Повний текст джерелаАнотація:
Hierarchical models provide a flexible way of modeling complex behavior. However, the complicated interdependencies among the parameters in the hierarchy make training such models difficult. MCMC methods have been widely used for this purpose, but can often only approximate the necessary distributions. Acceptance-rejection sampling allows for perfect simulation from these often unnormalized distributions by drawing from another distribution over the same support. The efficacy of acceptance-rejection sampling is explored through application to a small dataset which has been widely used for evaluating different methods for inference on hierarchical models. A particular algorithm is developed to draw variates from the posterior distribution. The algorithm is both verified and validated, and then finally applied to the given data, with comparisons to the results of different methods.
25
Fang, Fang. "A simulation study for Bayesian hierarchical model selection methods." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-2/fangf/fangfang.pdf.
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Tang, Yun. "Hierarchical Generalization Models for Cognitive Decision-making Processes." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1370560139.
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Song, Joon Jin. "Bayesian multivariate spatial models and their applications." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1122.
Повний текст джерелаАнотація:
Univariate hierarchical Bayes models are being vigorously researched for use in disease mapping, engineering, geology, and ecology. This dissertation shows how the models can also be used to build modelbased risk maps for areabased roadway traffic crashes. Countylevel vehicle crash records and roadway data from Texas are used to illustrate the method. A potential extension that uses univariate hierarchical models to develop networkbased risk maps is also discussed.
Several Bayesian multivariate spatial models for estimating the traffic crash rates from different types of crashes simultaneously are then developed. The specific class of spatial models considered is conditional autoregressive (CAR) model. The univariate CAR model is generalized for several multivariate cases. A general theorem for each case is provided to ensure that the posterior distribution is proper under improper and flat prior. The performance of various multivariate spatial models is compared using a Bayesian information criterion. The Markov chain Monte Carlo (MCMC) computational techniques are used for the model parameter estimation and statistical inference. These models are illustrated and compared again with the Texas crash data.
There are many directions in which this study can be extended. This dissertation concludes with a short summary of this research and recommends several promising extensions.
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Gschlössl, Susanne. "Hierarchical Bayesian spatial regression models with applications to non-life insurance." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=978924576.
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Pueschel, John. "The application of Bayesian hierarchical models to heterogeneous DNA profiling data." Thesis, University College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271228.
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Cybis, Gabriela Bettella. "Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/132858.
Повний текст джерелаАнотація:
Combining models for phenotypic and molecular evolution can lead to powerful inference tools. Under the flexible framework of Bayesian phylogenetics, I develop statistical methods to address phylodynamic problems in this intersection. First, I present a hierarchical phylogeographic method that combines information across multiple datasets to draw inference on a common geographical spread process. Each dataset represents a parallel realization of this geographic process on a different group of taxa, and the method shares information between these realizations through a hierarchical graph structure. Additionally, I develop a multivariate latent liability model for assessing phenotypic correlation among sets of traits, while controlling for shared evolutionary history. This method can efficiently estimate correlations between multiple continuous traits, binary traits and discrete traits with many ordered or unordered outcomes. Finally, I present a method that uses phylogenetic information to study the evolution of antigenic clusters in influenza. The method builds an antigenic cartography map informed by the assignment of each influenza strain to one of the antigenic clusters.
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Brody-Moore, Peter. "Bayesian Hierarchical Meta-Analysis of Asymptomatic Ebola Seroprevalence." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2228.
Повний текст джерелаАнотація:
The continued study of asymptomatic Ebolavirus infection is necessary to develop a more complete understanding of Ebola transmission dynamics. This paper conducts a meta-analysis of eight studies that measure seroprevalence (the number of subjects that test positive for anti-Ebolavirus antibodies in their blood) in subjects with household exposure or known case-contact with Ebola, but that have shown no symptoms. In our two random effects Bayesian hierarchical models, we find estimated seroprevalences of 8.76% and 9.72%, significantly higher than the 3.3% found by a previous meta-analysis of these eight studies. We also produce a variation of this meta-analysis where we exclude two of the eight studies. In this model, we find an estimated seroprevalence of 4.4%, much lower than our first two Bayesian hierarchical models. We believe a random effects model more accurately reflects the heterogeneity between studies and thus asymptomatic Ebola is more seroprevalent than previously believed among subjects with household exposure or known case-contact. However, a strong conclusion cannot be reached on the seriousness of asymptomatic Ebola without an international testing standard and more data collection using this adopted standard.
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Feldkircher, Martin, and Florian Huber. "Adaptive Shrinkage in Bayesian Vector Autoregressive Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4933/1/wp221.pdf.
Повний текст джерелаАнотація:
Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this paper we derive the shrinkage prior of Griffin et al. (2010) for the VAR case and its relevant conditional posterior distributions. This
framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariances of the VAR along with Gamma priors on a set of local and global prior scaling parameters. This prior setup is then generalized by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. A simulation exercise shows that the proposed framework yields more precise estimates of the model parameters and impulse response functions. In addition, a forecasting exercise applied to US data shows that the proposed prior outperforms other specifications in terms of point and density predictions. (authors' abstract)Series: Department of Economics Working Paper Series
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Knowles, David Arthur. "Bayesian non-parametric models and inference for sparse and hierarchical latent structure." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610403.
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Davies, Vinny. "Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7808/.
Повний текст джерелаАнотація:
Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation.
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Ho, Yu-Yun. "Diagnostics for hierarchical Bayesian regression models with application to repeated measures data /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487856906261615.
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Sheng, Yanyan. "Bayesian analysis of hierarchical IRT models comparing and combining the unidimensional & multi-unidimensional IRT models /." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4153.
Повний текст джерелаАнотація:
Thesis (Ph. D.)--University of Missouri-Columbia, 2005.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (July 19, 2006) Vita. Includes bibliographical references.
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Smith, Adam Nicholas. "Bayesian Analysis of Partitioned Demand Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497895561381294.
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Ren, Cuirong. "Topics in bayesian estimation : frequentist risks and hierarchical models for time to pregnancy /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025647.
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Lee, Duncan Paul. "Estimating the association between air pollution exposure and mortality using Bayesian hierarchical models." Thesis, University of Bath, 2007. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439177.
Повний текст джерелаАнотація:
This thesis develops statistical methodology for an important area of environmental epidemiology, that of the relationship between short-term exposure to air pollution and mortality or morbidity which has been a public health concern for over fifty years. The majority of studies investigating this relationship are based on ecological data, and estimate a group level association between ambient pollution levels and population aggregated mortality. This association is typically estimated with Poisson regression models, which make a number of simplifying assumptions about the underlying processes that generate the data. The work presented in this thesis extends the standard approaches to modelling these data in three main ways, the first proposing the use of autoregressive processes rather than smooth functions to remove any long-term trends and temporal correlation in the daily mortality series. The second extension relates to the pollution-mortality relationship, and investigates whether it changes over time rather than being constant or a dose-response curve. The remainder of this thesis investigates the importance of correctly estimating pollution exposures, and how mis-estimating them affects the resulting health risk. These extensions are implemented using Bayesian hierarchical models with estimation achieved via Markov chain monte carlo simulation. For the first two extensions likelihood based alternatives are also presented, using a combination of maximum likelihood and least squares methods. The thesis ends with a concluding discussion.
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Liu, Shiao. "Bayesian Analysis of Crime Survey Data with Nonresponse." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1175.
Повний текст джерелаАнотація:
Bayesian hierarchical models are effective tools for small area estimation by pooling small datasets together. The pooling procedures allow individual areas to “borrow strength” from each other to desirably improve the estimation. This work is an extension of Nandram and Choi (2002), NC, to perform inference on finite population proportions when there exists non-identifiability of the missing pattern for nonresponse in binary survey data. We review the small-area selection model (SSM) in NC which is able to incorporate the non-identifiability. Moreover, the proposed SSM, together with the individual-area selection model (ISM), and the small-area pattern-mixture model (SPM) are evaluated by real crime data in Stasny (1991). Furthermore, the methodology is compared to ISM and SPM using simulated small area datasets. Computational issues related to the MCMC are also discussed.
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Pfarrhofer, Michael, and Philipp Piribauer. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models." Elsevier, 2019. http://epub.wu.ac.at/6839/1/1805.10822.pdf.
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Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncertainty involve the use of Bayesian model-averaging techniques. However, Bayesian model-averaging for spatial autoregressive models suffers from severe drawbacks both in terms of computational time and possible extensions to more flexible econometric frameworks. To alleviate these problems, this paper presents two global-local shrinkage priors in the context of high-dimensional matrix exponential spatial specifications. A simulation study is conducted to evaluate the performance of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. Moreover, we use pan-European regional economic growth data to illustrate the performance of the proposed shrinkage priors.
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McBride, John Jacob Bratcher Thomas L. "Conjugate hierarchical models for spatial data an application on an optimal selection procedure /." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/3955.
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Fawcett, Lee, Neil Thorpe, Joseph Matthews, and Karsten Kremer. "A novel Bayesian hierarchical model for road safety hotspot prediction." Elsevier, 2016. https://publish.fid-move.qucosa.de/id/qucosa%3A72268.
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In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation – commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period – to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our model. We conclude that our model accurately predicts future accident counts, with point estimates from the predictive distribution matching observed counts extremely well.
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Weitzel, Nils [Verfasser]. "Climate field reconstructions from pollen and macrofossil syntheses using Bayesian hierarchical models / Nils Weitzel." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/120641779X/34.
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Adams, R. A. "Implications of hierarchical Bayesian models of the brain for the understanding of psychiatric disorders." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1429291/.
Повний текст джерелаАнотація:
This thesis explores how a hierarchical Bayesian model of the brain can explain aspects of schizophrenia and ‘functional’ motor and sensory symptoms (FMS). Bayesian computations prescribe the optimal integration of prior expectations with sensory evidence using the relative uncertainty (precision) of each source of information. The accurate representation of precision is crucial; its loss can lead to false inference by overweighting prior expectations or sensory evidence. Numerous phenomena in schizophrenia have been thought of as due to a loss of the brain’s predictive ability. This could be the result of a loss of precision of prior beliefs, through a reduction in synaptic gain at higher hierarchical levels, and I use this alteration to model the reduced mismatch negativity and three characteristic smooth pursuit eye movement (SPEM) abnormalities found in schizophrenia. My ultimate goal is to use the pursuit (and other) models to make inferences about the cortical encoding of precision. To establish the face validity of this approach, I tried to manipulate the precision estimates in normal subjects’ internal models, using a target moving with ‘smooth’ or ‘noisy’ (imprecise) velocity. I used subjects’ eye movements and DCM to invert the pursuit model and estimate the parameters of subjects’ internal models. I showed that noisy target velocity caused subjects to attend more to the sensory aspects of the stimulus (i.e. increased sensory precision). To demonstrate the construct validity of this pursuit DCM, I used magnetoencephalography (MEG) and DCM to test its prediction that noisy target motion increases sensory precision: corresponding to a decrease in the self-inhibition of superficial pyramidal cells in early visual cortex. Noisy motion decreased self-inhibition in central V1 at the group level, and in V2, changes in sensory precision in the pursuit DCM correlated with changes in V2 self-inhibition in the MEG DCM on an individual subject basis.
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Huddleston, Scott D. "Hitters vs. Pitchers: A Comparison of Fantasy Baseball Player Performances Using Hierarchical Bayesian Models." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3173.
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In recent years, fantasy baseball has seen an explosion in popularity. Major League Baseball, with its long, storied history and the enormous quantity of data available, naturally lends itself to the modern-day recreational activity known as fantasy baseball. Fantasy baseball is a game in which participants manage an imaginary roster of real players and compete against one another using those players' real-life statistics to score points. Early forms of fantasy baseball began in the early 1960s, but beginning in the 1990s, the sport was revolutionized due to the advent of powerful computers and the Internet. The data used in this project come from an actual fantasy baseball league which uses a head-to-head, points-based scoring system. The data consist of the weekly point totals that were accumulated over the first three-fourths of the 2011 regular season by the top 110 hitters and top 70 pitchers in Major League Baseball. The purpose of this project is analyze the relative value of pitchers versus hitters in this league using hierarchical Bayesian models. Three models will be compared, one which differentiates between hitters and pitchers, another which also differentiates between starting pitchers and relief pitchers, and a third which makes no distinction whatsoever between hitters and pitchers. The models will be compared using the deviance information criterion (DIC). The best model will then be used to predict weekly point totals for the last fourth of the 2011 season. Posterior predictive densities will be compared to actual weekly scores.
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Warn, David Edward. "Applications and extensions of Bayesian hierarchical models for meta-analysis of binary outcome data." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620461.
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Conlon, Erin Marie. "Estimation and flexible correlation structures in spatial hierarchical models of disease mapping /." Diss., ON-CAMPUS Access For University of Minnesota, Twin Cities Click on "Connect to Digital Dissertations", 1999. http://www.lib.umn.edu/articles/proquest.phtml.
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Marshall, Lucy Amanda Civil & Environmental Engineering Faculty of Engineering UNSW. "Bayesian analysis of rainfall-runoff models: insights to parameter estimation, model comparison and hierarchical model development." Awarded by:University of New South Wales. Civil and Environmental Engineering, 2006. http://handle.unsw.edu.au/1959.4/32268.
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One challenge that faces hydrologists in water resources planning is to predict the catchment???s response to a given rainfall. Estimation of parameter uncertainty (and model uncertainty) allows assessment of the risk in likely applications of hydrological models. Bayesian statistical inference, with computations carried out via Markov Chain Monte Carlo (MCMC) methods, offers an attractive approach to model specification, allowing for the combination of any pre-existing knowledge about individual models and their respective parameters with the available catchment data to assess both parameter and model uncertainty. This thesis develops and applies Bayesian statistical tools for parameter estimation, comparison of model performance and hierarchical model aggregation. The work presented has three main sections. The first area of research compares four MCMC algorithms for simplicity, ease of use, efficiency and speed of implementation in the context of conceptual rainfall-runoff modelling. Included is an adaptive Metropolis algorithm that has characteristics that are well suited to hydrological applications. The utility of the proposed adaptive algorithm is further expanded by the second area of research in which a probabilistic regime for comparing selected models is developed and applied. The final area of research introduces a methodology for hydrologic model aggregation that is flexible and dynamic. Rigidity in the model structure limits representation of the variability in the flow generation mechanism, which becomes a limitation when the flow processes are not clearly understood. The proposed Hierarchical Mixtures of Experts (HME) model architecture is designed to do away with this limitation by selecting individual models probabilistically based on predefined catchment indicators. In addition, the approach allows a more flexible specification of the model error to better assess the risk of likely outcomes based on the model simulations. Application of the approach to lumped and distributed rainfall runoff models for a variety of catchments shows that by assessing different catchment predictors the method can be a useful tool for prediction of catchment response.
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Abbas, Kaja Moinudeen. "Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5302/.
Повний текст джерелаАнотація:
Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with probability distributions of HIV surveillance data coupled with the census population data to estimate the proportion of HIV incidence among the different demographic subgroups. Demographic based risk analysis lends to observation of varied spectrum of HIV risk among the different demographic subgroups. A methodology using hidden Markov models is introduced that enables to investigate the impact of social behavioral interactions in the incidence and prevalence of infectious diseases. The methodology is presented in the context of simulated disease outbreak data for influenza. Probabilistic reasoning analysis enhances the understanding of disease progression in order to identify the critical points of surveillance, control and prevention. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest.