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1
Yang, Ming. "Hierarchical Bayesian topic modeling with sentiment and author extension." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/20598.
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Doctor of PhilosophyComputing and Information Sciences
William H. Hsu
While the Hierarchical Dirichlet Process (HDP) has recently been widely applied to topic modeling tasks, most current hybrid models for concurrent inference of topics and other factors are not based on HDP. In this dissertation, we present two new models that extend an HDP topic modeling framework to incorporate other learning factors. One model injects Latent Dirichlet Allocation (LDA) based sentiment learning into HDP. This model preserves the benefits of nonparametric Bayesian models for topic learning, while learning latent sentiment aspects simultaneously. It automatically learns different word distributions for each single sentiment polarity within each topic generated. The other model combines an existing HDP framework for learning topics from free text with latent authorship learning within a generative model using author list information. This model adds one more layer into the current hierarchy of HDPs to represent topic groups shared by authors, and the document topic distribution is represented as a mixture of topic distribution of its authors. This model automatically learns author contribution partitions for documents in addition to topics.
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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.
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Tak, Hyung Suk. "Topics in Bayesian Hierarchical Modeling and its Monte Carlo Computations." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493573.
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The first chapter addresses a Beta-Binomial-Logit model that is a Beta-Binomial conjugate hierarchical model with covariate information incorporated via a logistic regression. Various researchers in the literature have unknowingly used improper posterior distributions or have given incorrect statements about posterior propriety because checking posterior propriety can be challenging due to the complicated functional form of a Beta-Binomial-Logit model. We derive data-dependent necessary and sufficient conditions for posterior propriety within a class of hyper-prior distributions that encompass those used in previous studies. Frequency coverage properties of several hyper-prior distributions are also investigated to see when and whether Bayesian interval estimates of random effects meet their nominal confidence levels.
The second chapter deals with a time delay estimation problem in astrophysics. When the gravitational field of an intervening galaxy between a quasar and the Earth is strong enough to split light into two or more images, the time delay is defined as the difference between their travel times. The time delay can be used to constrain cosmological parameters and can be inferred from the time series of brightness data of each image. To estimate the time delay, we construct a Gaussian hierarchical model based on a state-space representation for irregularly observed time series generated by a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian approach jointly infers model parameters via a Gibbs sampler. We also introduce a profile likelihood of the time delay as an approximation of its marginal posterior distribution.
The last chapter specifies a repelling-attracting Metropolis algorithm, a new Markov chain Monte Carlo method to explore multi-modal distributions in a simple and fast manner. This algorithm is essentially a Metropolis-Hastings algorithm with a proposal that consists of a downhill move in density that aims to make local modes repelling, followed by an uphill move in density that aims to make local modes attracting. The downhill move is achieved via a reciprocal Metropolis ratio so that the algorithm prefers downward movement. The uphill move does the opposite using the standard Metropolis ratio which prefers upward movement. This down-up movement in density increases the probability of a proposed move to a different mode.Statistics
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Zhuang, Lili. "Bayesian Dynamical Modeling of Count Data." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315949027.
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Porter, Aaron Thomas. "A path-specific approach to SEIR modeling." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2963.
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Despite being developed in the late 1920s, compartmental epidemic modeling is still a rich and fruitful area of research. The original compartmental epidemic models were SIR (Susceptible, Infectious, Removed) models, which assume permanent immunity after recovery. SIR models, along with the more recent SEIR (Susceptible, Exposed, Infectious, Removed) models are still the gold standard in modeling pathogens that confer permanent immunity. This dissertation expands the SEIR structure to include a new class of spatial SEIR models. The exponential assumption of these models states that the latent and infectious times of the pathogen are exponentially distributed. Work that relaxes this assumption and still allows for mixing to occur at the population level is limited, thereby making strong assumptions about these times. We relax this assumption in a flexible way, by considering a hybrid approach that contains characteristics of both population level and individual level approaches. Next, we expand the Conditional Autoregressive (CAR) class of spatial models. This is to account for the Mumps data set we have procured, which contains mismatched lattice structures that cannot be handled by traditional CAR models. The use of CAR models is desirable here, as these models are known to produce spatial smoothing on lattices, and are a natural way to draw strength spatially in estimating spatial effects. Finally, we develop a pair of spatial SEIR models utilizing our CAR structure. The first utilizes the exponential assumption, which is very robust. The second develops a highly flexible spatial SEIR model by embedding the CAR structure into the SEIR structure. This allows for a realistic analysis of epidemic data occurring on a lattice. These models are applied to the Iowa Mumps epidemic of 2006. There are three questions of interest. First, what improvement do the methods proposed here provide over the current models in the literature? Second, did spring break, which occurred approximately 40 days into the epidemic, have an effect on the overall number of new infections? Thirdly, did the public's awareness of the epidemic change the rate at which mixing occurred over time? The spatial models in this dissertation are adequately constructed to answer these questions, and the results are provided.
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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.
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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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Mehl, Christopher. "Bayesian Hierarchical Modeling and Markov Chain Simulation for Chronic Wasting Disease." Diss., University of Colorado at Denver, 2004. http://hdl.handle.net/10919/71563.
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In this thesis, a dynamic spatial model for the spread of Chronic Wasting Disease in Colorado mule deer is derived from a system of differential equations that captures the qualitative spatial and temporal behaviour of the disease. These differential equations are incorporated into an empirical Bayesian hierarchical model through the unusual step of deterministic autoregressive updates. Spatial effects in the model are described directly in the differential equations rather than through the use of correlations in the data. The use of deterministic updates is a simplification that reduces the number of parameters that must be estimated, yet still provides a flexible model that gives reasonable predictions for the disease. The posterior distribution generated by the data model hierarchy possesses characteristics that are atypical for many Markov chain Monte Carlo simulation techniques. To address these difficulties, a new MCMC technique is developed that has qualities similar to recently introduced tempered Langevin type algorithms. The methodology is used to fit the CWD model, and posterior parameter estimates are then used to obtain predictions about Chronic Wasting Disease.
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Monson, Rebecca Lee. "Modeling Transition Probabilities for Loan States Using a Bayesian Hierarchical Model." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2179.pdf.
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Huo, Shuning. "Bayesian Modeling of Complex High-Dimensional Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101037.
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With the rapid development of modern high-throughput technologies, scientists can now
collect high-dimensional complex data in different forms, such as medical images, genomics
measurements. However, acquisition of more data does not automatically lead to better
knowledge discovery. One needs efficient and reliable analytical tools to extract useful information
from complex datasets. The main objective of this dissertation is to develop innovative
Bayesian methodologies to enable effective and efficient knowledge discovery from
complex high-dimensional data. It contains two parts—the development of computationally
efficient functional mixed models and the modeling of data heterogeneity via Dirichlet
Diffusion Tree. The first part focuses on tackling the computational bottleneck in Bayesian
functional mixed models. We propose a computational framework called variational functional
mixed model (VFMM). This new method facilitates efficient data compression and
high-performance computing in basis space. We also propose a new multiple testing procedure
in basis space, which can be used to detect significant local regions. The effectiveness
of the proposed model is demonstrated through two datasets, a mass spectrometry dataset
in a cancer study and a neuroimaging dataset in an Alzheimer's disease study. The second
part is about modeling data heterogeneity by using Dirichlet Diffusion Trees. We propose
a Bayesian latent tree model that incorporates covariates of subjects to characterize the
heterogeneity and uncover the latent tree structure underlying data. This innovative model
may reveal the hierarchical evolution process through branch structures and estimate systematic
differences between groups of samples. We demonstrate the effectiveness of the model
through the simulation study and a brain tumor real data.Doctor of Philosophy
With the rapid development of modern high-throughput technologies, scientists can now collect high-dimensional data in different forms, such as engineering signals, medical images, and genomics measurements. However, acquisition of such data does not automatically lead to efficient knowledge discovery. The main objective of this dissertation is to develop novel Bayesian methods to extract useful knowledge from complex high-dimensional data. It has two parts—the development of an ultra-fast functional mixed model and the modeling of data heterogeneity via Dirichlet Diffusion Trees. The first part focuses on developing approximate Bayesian methods in functional mixed models to estimate parameters and detect significant regions. Two datasets demonstrate the effectiveness of proposed method—a mass spectrometry dataset in a cancer study and a neuroimaging dataset in an Alzheimer's disease study. The second part focuses on modeling data heterogeneity via Dirichlet Diffusion Trees. The method helps uncover the underlying hierarchical tree structures and estimate systematic differences between the group of samples. We demonstrate the effectiveness of the method through the brain tumor imaging data.
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McHugh, Sean W. "Phylogenetic Niche Modeling." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104893.
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Projecting environmental niche models through time is a common goal when studying species response to climatic change. Species distribution models (SDMs) are commonly used to estimate a species' niche from observed patterns of occurrence and environmental predictors. However, a species niche is also shaped by non-environmental factors--including biotic interactions and dispersal barrier—truncating SDM estimates. Though truncated SDMs may accurately predict present-day species niche, projections through time are often biased by environmental condition change. Modeling niche in a phylogenetic framework leverages a clade's shared evolutionary history to pull species estimates closer towards phylogenetic conserved values and farther away from species specific biases. We propose a new Bayesian model of phylogenetic niche implemented in R. Under our model, species SDM parameters are transformed into biologically interpretable continuous parameters of environmental niche optimum, breadth, and tolerance evolving under multivariate Brownian motion random walk. Through simulation analyses, we demonstrated model accuracy and precision that improved as phylogeny size increased. We also demonstrated our model on a clade of eastern United States Plethodontid salamanders by accurately estimating species niche, even when no occurrence data is present. Our model demonstrates a novel framework where niche changes can be studied forwards and backwards through time to understand ancestral ranges, patterns of environmental specialization, and niche in data deficient species.Master of Science
As many species face increasing pressure in a changing climate, it is crucial to understand the set of environmental conditions that shape species' ranges--known as the environmental niche--to guide conservation and land management practices. Species distribution models (SDMs) are common tools that are used to model species' environmental niche. These models treat a species' probability of occurrence as a function of environmental conditions. SDM niche estimates can predict a species' range given climate data, paleoclimate, or projections of future climate change to estimate species range shifts from the past to the future. However, SDM estimates are often biased by non-environmental factors shaping a species' range including competitive divergence or dispersal barriers. Biased SDM estimates can result in range predictions that get worse as we extrapolate beyond the observed climatic conditions. One way to overcome these biases is by leveraging the shared evolutionary history amongst related species to "fill in the gaps". Species that are more closely phylogenetically related often have more similar or "conserved" environmental niches. By estimating environmental niche over all species in a clade jointly, we can leverage niche conservatism to produce more biologically realistic estimates of niche. However, currently a methodological gap exists between SDMs estimates and macroevolutionary models, prohibiting them from being estimated jointly. We propose a novel model of evolutionary niche called PhyNE (Phylogenetic Niche Evolution), where biologically realistic environmental niches are fit across a set of species with occurrence data, while simultaneously fitting and leveraging a model of evolution across a portion of the tree of life. We evaluated model accuracy, bias, and precision through simulation analyses. Accuracy and precision increased with larger phylogeny size and effectively estimated model parameters. We then applied PhyNE to Plethodontid salamanders from Eastern North America. This ecologically-important and diverse group of lungless salamanders require cold and wet conditions and have distributions that are strongly affected by climatic conditions. Species within the family vary greatly in distribution, with some species being wide ranging generalists, while others are hyper-endemics that inhabit specific mountains in the Southern Appalachians with restricted thermal and hydric conditions. We fit PhyNE to occurrence data for these species and their associated average annual precipitation and temperature data. We identified no correlations between species environmental preference and specialization. Pattern of preference and specialization varied among Plethodontid species groups, with more aquatic species possessing a broader environmental niche, likely due to the aquatic microclimate facilitating occurrence in a wider range of conditions. We demonstrated the effectiveness of PhyNE's evolutionarily-informed estimates of environmental niche, even when species' occurrence data is limited or even absent. PhyNE establishes a proof-of-concept framework for a new class of approaches for studying niche evolution, including improved methods for estimating niche for data-deficient species, historical reconstructions, future predictions under climate change, and evaluation of niche evolutionary processes across the tree of life. Our approach establishes a framework for leveraging the rapidly growing availability of biodiversity data and molecular phylogenies to make robust eco-evolutionary predictions and assessments of species' niche and distributions in a rapidly changing world.
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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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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.
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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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Wang, Xiaohui. "Bayesian classification and survival analysis with curve predictors." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1205.
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Li, Linhua. "A GIS-based Bayesian approach for analyzing spatial-temporal patterns of traffic crashes." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1766.
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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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Tao, Shuqin. "Using collateral information in the estimation of sub-scores --- a fully Bayesian approach." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/321.
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Educators and administrators often use sub-scores derived from state accountability assessments to diagnose learning/instruction and inform curriculum planning. However, there are several psychometric limitations of observed sub-scores, two of which were the focus of the present study: (1) limited reliabilities due to short lengths, and (2) little distinct information in sub-scores for most existing assessments.
The present study was conducted to evaluate the extent to which these limitations might be overcome by incorporating collateral information into sub-score estimation. The three sources of collateral information under investigation included (1) information from other sub-scores, (2) schools that students attended, and (3) school-level scores on the same test taken by previous cohorts of students in that school. Kelley's and Shin's methods were implemented in a fully Bayesian framework and were adapted to incorporate differing levels of collateral information. Results were evaluated in light of three comparison criteria, i.e., signal noise ratio, standard error of estimate, and sub-score separation index. The data came from state accountability assessments.
Consistent with the literature, using information from other sub-scores produced sub-scores with enhanced precision but reduced profile variability. This finding suggests that using collateral information internal to the test has the capability of enhancing sub-score reliability, but at the expense of losing the distinctness of each individual sub-score. Using information indicating the schools that students attended led to a small gain in sub-score precision without losing sub-score distinctness. Furthermore, using such information was found to have the potential to improve sub-score validity by addressing Simpson's paradox when sub-score correlations were not invariant across schools. Using previous-year school-level sub-score information was found to have the potential to enhance both precision and distinctness for school-level sub-scores, although not for student-level sub-scores. School-level sub-scores were found to exhibit satisfactory psychometric properties and thus have value in evaluating school curricular effectiveness. Issues concerning validity, interpretability, suitability of using such collateral information are discussed in the context of state accountability assessments.
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Tolwinski-Ward, Susan E. "Inference on Tree-Ring Width and Paleoclimate Using a Proxy Model of Intermediate Complexity." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/241975.
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Forward and inverse modeling studies of the relationship between tree ring width and bivariate climate are performed using a model called VS-Lite. The monthly time-step model incorporates two simple but realistic nonlinearities in its description of the transformation of climate variability into ring width index. These features ground VS-Lite in scientific principles and make it more complex than empirically-derived statistical models commonly used to simulate tree ring width. At the same time, VS-Lite is vastly simpler and more efficient than pre-existing numerical models that simulate detailed biological aspects of tree growth. A forward modeling validation study shows that VS-Lite simulates a set of observed chronologies across the continental United States with comparable or better skill than simulations derived from a standard, linear regression based approach. This extra skill derives from VS-Lite's basis in mechanistic principles, which makes it more robust than the statistical methodology to climatic nonstationarity. A Bayesian parameterization approach is also developed that incorporates scientific information into the choice of locally optimal VS-Lite parameters. The parameters derived using the scheme are found to be interpretable in terms of the climate controls on growth, and so provide a means to guide applications of the model across varying climatologies. The first reconstructions of paleoclimate that assimilate scientific understanding of the ring width formation process are performed using VS-Lite to link the proxy data to potential climate histories. Bayesian statistical methods invert VS-Lite conditional on a given dendrochronolgy to produce probabilistic estimates of local bivariate climate. Using VS-Lite in this manner produces skillful estimates, but does not present advantages compared another set of probabilistic reconstructions that invert a simpler, linear, empirical forward model. This result suggests that future data-assimilation based reconstructions will need to integrate as many data sources as possible, both across space and proxy types, in order to benefit from information provided by mechanistic models of proxy formation.
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Li, Xia. "A Bayesian Hierarchical Model for Studying Inter-Occasion and Inter-Subject Variability in Pharmacokinetics." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1296592410.
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Milo, Michael William. "Anomaly Detection in Heterogeneous Data Environments with Applications to Mechanical Engineering Signals & Systems." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23962.
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Anomaly detection is a relevant problem in the field of Mechanical Engineering, because the analysis of mechanical systems often relies on identifying deviations from what is considered "normal". The mechanical sciences are represented by a heterogeneous collection of data types: some systems may be highly dimensional, may contain exclusively spatial or temporal data, may be spatiotemporally linked, or may be non-deterministic and best described probabilistically. Given the broad range of data types in this field, it is not possible to propose a single processing method that will be appropriate, or even usable, for all data types. This has led to human observation remaining a common, albeit costly and inefficient, approach to detecting anomalous signals or patterns in mechanical data.
The advantages of automated anomaly detection in mechanical systems include reduced monitoring costs, increased reliability of fault detection, and improved safety for users and operators. This dissertation proposes a hierarchical framework for anomaly detection through machine learning, and applies it to three distinct and heterogeneous data types: state-based data, parameter-driven data, and spatiotemporal sensor network data. In time-series data, anomaly detection results were robust in synthetic data generated using multiple simulation algorithms, as well as experimental data from rolling element bearings, with highly accurate detection rates (>99% detection, <1% false alarm). Significant developments were shown in parameter-driven data by reducing the sample sizes necessary for analysis, as well as reducing the time required for computation. The event-space model extends previous work into a geospatial sensor network and demonstrates applications of this type of event modeling at various timescales, and compares the model to results obtained using other approaches.
Each data type is processed in a unique way relative to the others, but all are fitted to the same hierarchical structure for system modeling. This hierarchical model is the key development proposed by this dissertation, and makes both novel and significant contributions to the fields of mechanical analysis and data processing. This work demonstrates the effectiveness of the developed approaches, details how they differ from other relevant industry standard methods, and concludes with a proposal for additional research into other data types.Ph. D.
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Rouillard, Louis. "Bridging Simulation-based Inference and Hierarchical Modeling : Applications in Neuroscience." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG024.
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La neuroimagerie étudie l'architecture et le fonctionnement du cerveau à l'aide de la résonance magnétique (IRM). Pour comprendre le signal complexe observé, les neuroscientifiques émettent des hypothèses sous la forme de modèles explicatifs, régis par des paramètres interprétables. Cette thèse étudie l'inférence statistique : deviner quels paramètres auraient pu produire le signal à travers le modèle.L'inférence en neuroimagerie est complexifiée par au moins trois obstacles : une grande dimensionnalité, une grande incertitude et la structure hiérarchique des données. Pour s'attaquer à ce régime, nous utlisons l'inférence variationnelle (VI), une méthode basée sur l'optimisation.Plus précisément, nous combinons l'inférence variationnelle stochastique structurée et les flux de normalisation (NF) pour concevoir des familles variationnelles expressives et adaptées à la large dimensionnalité. Nous appliquons ces techniques à l'IRM de diffusion et l'IRM fonctionnelle, sur des tâches telles que la parcellation individuelle, l'inférence de la microstructure et l'estimation du couplage directionnel. Via ces applications, nous soulignons l'interaction entre les divergences de Kullback-Leibler (KL) forward et reverse comme outils complémentaires pour l'inférence. Nous démontrons également les capacité de l'inférence variationelle automatique (AVI) comme méthode d'inférence robuste et adaptée à la large dimensionnalité, apte à relever les défis de la modélisation en neuroscienceNeuroimaging investigates the brain's architecture and function using magnetic resonance (MRI). To make sense of the complex observed signal, Neuroscientists posit explanatory models, governed by interpretable parameters. This thesis tackles statistical inference : guessing which parameters could have yielded the signal through the model.Inference in Neuroimaging is complexified by at least three hurdles : a large dimensionality, a large uncertainty, and the hierarchcial structure of data. We look into variational inference (VI) as an optimization-based method to tackle this regime.Specifically, we conbine structured stochastic VI and normalizing flows (NFs) to design expressive yet scalable variational families. We apply those techniques in diffusion and functional MRI, on tasks including individual parcellation, microstructure inference and directional coupling estimation. Through these applications, we underline the interplay between the forward and reverse Kullback-Leibler (KL) divergences as complemen-tary tools for inference. We also demonstrate the ability of automatic VI (AVI) as a reliable and scalable inference method to tackle the challenges of model-driven Neuroscience
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Schaper, Andrew. "Informative Prior Distributions in Multilevel/Hierarchical Linear Growth Models: Demonstrating the Use of Bayesian Updating for Fixed Effects." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18366.
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This study demonstrates a fully Bayesian approach to multilevel/hierarchical linear growth modeling using freely available software. Further, the study incorporates informative prior distributions for fixed effect estimates using an objective approach. The objective approach uses previous sample results to form prior distributions included in subsequent samples analyses, a process referred to as Bayesian updating. Further, a method for model checking is outlined based on fit indices including information criteria (i.e., Akaike information criterion, Bayesian information criterion, and deviance information criterion) and approximate Bayes factor calculations. For this demonstration, five distinct samples of schools in the process of implementing School-Wide Positive Behavior Interventions and Supports (SWPBIS) collected from 2008 to 2013 were used with the unit of analysis being the school. First, the within-year SWPBIS fidelity growth was modeled as a function of time measured in months from initial measurement occasion. Uninformative priors were used to estimate growth parameters for the 2008-09 sample, and both uninformative and informative priors based on previous years' samples were used to model data from the 2009-10, 2010-11, 2011-12, 2012-13 samples. Bayesian estimates were also compared to maximum likelihood (ML) estimates, and reliability information is provided. Second, an additional three examples demonstrated how to include predictors into the growth model with demonstrations for: (a) the inclusion of one school-level predictor (years implementing) of SWPBIS fidelity growth, (b) several school-level predictors (relative socio-economic status, size, and geographic location), and (c) school and district predictors (sustainability factors hypothesized to be related to implementation processes) in a three-level growth model. Interestingly, Bayesian models estimated with informative prior distributions in all cases resulted in more optimal fit indices than models estimated with uninformative prior distributions.
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Fang, Youjia. "Modeling Driving Risk Using Naturalistic Driving Study Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/65151.
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Motor vehicle crashes are one of the leading causes of death in the United States. Traffic safety research targets at understanding the cause of crash, preventing the crash, and mitigating crash severity. This dissertation focuses on the driver-related traffic safety issues, in particular, on developing and implementing contemporary statistical modeling techniques on driving risk research on Naturalistic Driving Study data. The dissertation includes 5 chapters. In Chapter 1, I introduced the backgrounds of traffic safety research and naturalistic driving study. In Chapter 2, the state-of-practice statistical methods were implemented on individual driver risk assessment using NDS data. The study showed that critical-incident events and driver demographic characteristics can serve as good predictors for identifying risky drivers. In Chapter 3, I developed and evaluated a novel Bayesian random exposure method for Poisson regression models to account for situations where the exposure information needs to be estimated. Simulation studies and real data analysis on Cellphone Pilot Analysis study data showed that, random exposure models have significantly better model fitting performances and higher parameter coverage probabilities as compared to traditional fixed exposure models. The advantage is more apparent when the values of Poisson regression coefficients are large. In Chapter 4, I performed comprehensive simulation-based performance analyses to investigate the type-I error, power and coverage probabilities on summary effect size in classical meta-analysis models. The results shed some light for reference on the prospective and retrospective performance analysis in meta-analysis research. In Chapter 5, I implemented classical- and Bayesian-approach multi-group hierarchical models on 100-Car data. Simulation-based retrospective performance analyses were used to investigate the powers and parameter coverage probabilities among different hierarchical models. The results showed that under fixed-effects model context, complex secondary tasks are associated with higher driving risk.Ph. D.
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Spencer, Thomas Louis. "Enhanced Air Transportation Modeling Techniques for Capacity Problems." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/82353.
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Effective and efficient air transportation systems are crucial to a nation's economy and connectedness. These systems involve capital-intensive facilities and equipment and move millions of people and tonnes of freight every day. As air traffic has continued to increase, the systems necessary to ensure safe and efficient operation will continue to grow more and more complex. Hence, it is imperative that air transport analysts are equipped with the best tools to properly predict and respond to expected air transportation operations. This dissertation aims to improve on those tools currently available to air transportation analysts, while offering new ones.
Specifically, this thesis will offer the following: 1) A model for predicting arrival runway occupancy times (AROT); 2) a model for predicting departure runway occupancy times (DROT); and 3) a flight planning model. This thesis will also offer an exploration of the uses of unmanned aerial vehicles for providing wireless communications services.
For the predictive models of AROT and DROT, we fit hierarchical Bayesian regression models to the data, grouped by aircraft type using airport physical and aircraft operational parameters as the regressors. Recognizing that many existing air transportation models require distributions of AROT and DROT, Bayesian methods are preferred since their output are distributions that can be directly inputted into air transportation modeling programs. Additionally, we exhibit how analysts will be able to decouple AROT and DROT predictions from the traditional 4 or 5 groupings of aircraft currently in use.
Lastly, for the flight planning model, we present a 2-D model using presently available wind data that provides wind-optimal flight routings. We improve over current models by allowing free-flight unconnected to pre-existing airways and by offering finer resolutions over the current 2.5 degree norm.Ph. D.
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Kim, Youngho. "A surveillance modeling and ecological analysis of urban residential crimes in Columbus, Ohio, using Bayesian Hierarchical data analysis and new space-time surveillance methodology." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1186607028.
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Agarwal, Kuldeep. "Physics Based Hierarchical Decomposition of Processes for Design of Complex Engineered Systems." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322152146.
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Brynjarsdóttir, Jenný. "Dimension Reduced Modeling of Spatio-Temporal Processes with Applications to Statistical Downscaling." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312935520.
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Ning, Shuluo. "Bayesian Degradation Analysis Considering Competing Risks and Residual-Life Prediction for Two-Phase Degradation." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1339559200.
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Osth, Adam Frederick. "Sources of interference in item and associative recognition memory: Insights from a hierarchical Bayesian analysis of a global matching model." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397136173.
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Macedo, Leandro Roberto de. "Modelagem hierárquica Bayesiana na avaliação de curvas de crescimento de suínos genotipados para o gene halotano." Universidade Federal de Viçosa, 2013. http://locus.ufv.br/handle/123456789/4072.
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Previous issue date: 2013-07-31A hierarchical Bayesian modeling was used to evaluate the influence of halothane gene and its interaction with sex on pig ́s growth curves. Under this approach, the parameters from growth models (Logistic, Gompertz and von Bertalanffy) were estimated jointly with the effects of halothane gene and sex. A total of 344 F2 (Commercial x Piau) animals were weighted at birth, 21, 42, 63, 77, 105 and 150 days in life. The Logistic model has presented the best fit based on DIC (Deviance Information Criterion). Thus, the samples from marginal posterior distributions for the differences between the parameters estimates of Logistic model have indicated that the maturity weight of males with heterozygous genotypes (HALNn) was superior to males with homozygous genotypes (HALNN). In order to realize a comparison with the traditional methodology, the frequentist approach based on two distinct steps also was used, but there was not identified significant differences between growth curve parameter estimates from each group (combinations of halothane genotypes and sex).
Para avaliar a influência do gene halotano sobre a curva de crescimento de suínos, bem como sua interação com o sexo do animal, foi proposta uma modelagem hierárquica Bayesiana. Nesta abordagem, os parâmetros dos modelos não-lineares de crescimento (Logístico, Gompertz e von Bertalanffy) foram estimados conjuntamente com os efeitos de sexo e genótipos do gene halotano. Foram utilizados 344 animais F2(Comercial x Piau) pesados ao nascer, aos 21, 42, 63, 77, 105 e 150 dias. O modelo Logístico foi aquele que apresentou melhor qualidade de ajuste por apresentar menor DIC (Deviance Information Criterion) que os demais. As amostras das distribuições marginais a posteriori para as diferenças entre as estimativas dos parâmetros do modelo Logístico indicaram que o peso dos machos à idade adulta com genótipo heterozigoto (HALNn) foi superior ao dos homozigotos (HALNN). A título de comparação, também foi considerada a abordagem frequentista tradicional baseada em dois passos distintos, a qual, por apresentar um menor poder de discernimento estatístico, não mostrou diferenças significativas.
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Han, Gang. "Modeling the output from computer experiments having quantitative and qualitative input variables and its applications." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228326460.
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Race, Jonathan Andrew. "Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077.
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Smith, Michael Ross. "Modeling the Performance of a Baseball Player's Offensive Production." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1189.pdf.
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Feldkircher, Martin, Florian Huber, and Gregor Kastner. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?" WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6021/1/wp260.pdf.
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We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets while simpler models tend to perform better in sizeable data sets. To combine best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time.Series: Department of Economics Working Paper Series
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Wu, Xinying. "Reliability Assessment of a Continuous-state Fuel Cell Stack System with Multiple Degrading Components." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1556794664723115.
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SUI, ZHENHUAN. "Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1499446404436637.
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Katzfuss, Matthias. "Hierarchical Spatial and Spatio-Temporal Modeling of Massive Datasets, with Application to Global Mapping of CO2." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1308316063.
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Hanandeh, Ahmad Ali. "Nonstationary Nearest Neighbors Gaussian Process Models." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781089107666.
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Flask, Thomas V. "An Application of Multi-Level Bayesian Negative Binomial Models with Mixed Effects on Motorcycle Crashes in Ohio." University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1333046055.
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Poznyak, Dmytro. "The American Attitude: Priming Issue Agendas and Longitudinal Dynamic of Political Trust." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342715776.
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Muncy, Brenee' Lynn. "THE EFFECTS OF MOUNTAINTOP REMOVAL MINING AND VALLEY FILLS ON STREAM SALAMANDER COMMUNITIES." UKnowledge, 2014. http://uknowledge.uky.edu/forestry_etds/15.
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Mountaintop removal mining and valley filling (MTR/VF) is a common form of land conversion in Central Appalachia and threatens the integrity of stream ecosystems. We investigated the effects of MTR/VF on stream salamander occupancy probabilities and community structure by conducting area constrained active searches for stream salamanders within intermittent streams located in mature forest (i.e., control) and those impacted by MTR/VF. During March to June of 2013, we detected five stream salamander species (Desmognathus fuscus, D. monticol, Eurycea cirrigera, Pseudotriton ruber, and Gyrinophilus porphyriticus) and found that the probability of occupancy was greatly reduced in MTR/VF streams compared to control streams. Additionally, the salamander community was greatly reduced in MTR/VF streams; the mean species richness estimate for MTR/VF streams was 2.09 (± 1.30 SD), whereas richness was 4.83 (± 0.58 SD) for control streams. Numerous mechanisms may be responsible for decreased occupancy and diminished salamander communities at MTR/VF streams, although water chemistry of streams may be a particularly important mechanism. Indeed, we detected elevated levels of specific conductivity, pH, total organic carbon, and dissolved ions in MTR/VF streams. Our results indicate that salamander communities, with other invertebrates, fish, and other aquatic and/or semi-aquatic animals, are susceptible to MTR/VF mining practices.
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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 the dual response system so that the inherent hierarchical variance structure of the response can be modeled naturally. The Bayesian model is developed for both univariate and multivariate dual response surfaces, and for both fully replicated and partially replicated dual response surface designs. To evaluate its performance, the Bayesian method has been compared with the original method under a wide range of scenarios, and it shows higher efficiency and more robustness. In applications, the Bayesian approach retains all the advantages provided by the original dual response surface modelling method. Moreover, the Bayesian analysis allows inference on the uncertainty of the model parameters, and thus can give practitioners complete information on the distribution of the characteristic of interest.Ph. D.
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Rajeev, Deepthi. "Separate and Joint Analysis of Longitudinal and Survival Data." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1775.pdf.
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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 uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.
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Heydari, Jonathan. "Bayesian hierarchical modelling for inferring genetic interactions in yeast." Thesis, University of Newcastle upon Tyne, 2014. http://hdl.handle.net/10443/2464.
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Identifying genetic interactions for a given microorganism, such as yeast, is difficult. Quantitative Fitness Analysis (QFA) is a high-throughput experimental and computa tional methodology for quantifying the fitness of microbial cultures. QFA can be used to compare between fitness observations for different genotypes and thereby infer genetic interaction strengths. Current “naive” frequentist statistical approaches used in QFA do not model between-genotype variation or difference in genotype variation under differ ent conditions. In this thesis, a Bayesian approach is introduced to evaluate hierarchical models that better reflect the structure or design of QFA experiments. First, a two-stage approach is presented: a hierarchical logistic model is fitted to microbial culture growth curves and then a hierarchical interaction model is fitted to fitness summaries inferred for each genotype. Next, a one-stage Bayesian approach is presented: a joint hierarchi cal model which simultaneously models fitness and genetic interaction, thereby avoiding passing information between models via a univariate fitness summary. The new hierarchical approaches are then compared using a dataset examining the effect of telomere defects on yeast. By better describing the experimental structure, new evidence is found for genes and complexes which interact with the telomere cap. Various extensions of these models, including models for data transformation, batch effects and intrinsically stochastic growth models are also considered.
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Wu, JenHao. "Reliability analysis for small wind turbines using Bayesian hierarchical modelling." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29015.
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In this thesis, the reliability of small wind turbines is studied. Both conventional reliability analysis methods and the novel Bayesian models (Bayesian Hierarchical Modelling (BHM)) are used to analyse the reliability performance of the Gaia-Wind turbines / assemblies and components of the Gaia-Wind turbine. In Chapter 2, a simple failure mode and effect analysis (FMEA) is conducted. An approximated risk priority number (RPN) is calculated for each failure mode and assembly. The assembly that is identified to have the highest RPN is the "Rotor and Blade Assembly". As for the failure modes, "Blade Split" and "Generator Failure" failure modes are identified to have the highest RPNs. In Chapter 3, the conventional methods including the Kaplan-Meier Analysis, Weibull Plot Analysis, Homogeneous Poisson Process (HPP) Analysis, and Crow-AMSAA (Non-Homogeneous Poisson Process (NHPP)) Analysis are used to study the reliability performance of the generic turbine and the critical assemblies based on the approximated RPNs. By using these conventional methods, the L10 life can be approximated (Kaplan-Meier), the main failure modes of an assembly can be identified (Weibull Plot Analysis), the annual failure rate can be estimated (HPP), and the number of future failures can be predicted (NHPP). These methods have been implemented in a novel on-line interactive platform, named ReliaOS (Chapter 7), which effectively facilitates the process of converting the information in the warranty record to the meaningful reliability information. Three novel BHM models are proposed and implemented in WinBUGS (an open source software), namely the repair model, the environmental model, and the informative prior framework, (Chapter 5 and Chapter 6). The repair model is used to quantify the repair effectiveness of a generic repair action. The model is applied on both the turbine level as well as the component level. At the turbine level, the annual failure rate of the generic turbine is predicted to be 0:159 per turbine per year at the first year. Individual turbines can be categorised into different quality levels ("Good", "Good- Normal", "Normal", "Normal-Bad", and "Bad") based on the predicted annual failure rate values. At the component level, "Blade split", "Cracked Frame", and "Generator Failure" failure modes are studied. These are the most critical failure modes for "Rotor and Blade Assembly", "Tower, Foundation, and Nacelle", and "Generator" assemblies respectively. "Cracked Frame" failure mode is predicted to have the lowest characteristic life and a slightly increasing failure rate trend. The repair effectiveness of the "Cracked Frame" failure mode is identified to be slightly ineffective. The environmental model quantifies the influence of three environmental covariates, i.e. AverageWind Speed (AWS), Turbulence Intensity (TI), and Terrain Slope (TS). These environmental covariates are all identified to have negative impact to the reliability of the generic turbine, where TI and AWS have more pronounced impact than TS. The informative prior BHM framework offers a way of quantifying the reliability of the drivetrain frame (which corresponds to the "Cracked Frame" failure mode) in a situation where zero failure instance is recorded for the new drivetrain frame design. This is achieved by jointly considering the simulation results from SOLIDWORKS as the prior information into the BHM model. This thesis strives to understand the reliability performance of the Gaia-Wind small wind turbine from different perspectives, i.e. the generic turbine, individual turbines, and the components, by the use of conventional methods and the proposed BHM models. The novel on-line reliability platform, ReliaOS, mitigates the difficulties in converting the information in the data to the reliability information for the end users. It is believed that the proposed BHM models and the ReliaOS on-line reliability analysis platform will improve the reliability analysis of small-wind turbines.
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Lin, Qihua. "Bayesian hierarchial spatiotemporal modeling of functional magnetic resonance imaging data." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3245023.
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Thesis (Ph.D. in Statistical Science)--S.M.U., 2007.Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7154. Adviser: Richard F. Gunst. Includes bibliographical references.
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Taglioni, Charlotte. "Bayesian hierarchical modelling for population size estimation: application to Italian data." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424971.
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Bayesian demography developments, global trends for substituting traditional censuses with cheaper methods able to use available information, and new technologies require investigating and providing new models to answer new requirements. In Italy in particular, during the last years Istat worked for launching in October 2018 the ``permanent census of population and housing''. After a first discussion on censuses, changes recommended by organisations such as the UN and the European Union to the National Statistical Institutes, and on new demographic models for population size estimation, the model proposed by Bryant and Graham (2013) is analysed. The model allows for integration of different data sources, for demographic series estimation, and it is very flexible and complex at the same time. Applications of this model to the Italian population are performed, highlighting its advantages and limits. Data for the period considered (2006-2015) and metadata come from Istat. Data are not always consistent, confirming the need of statistical methods able to integrate sources and reconstruct demographic series. As expected, census data and migration flows estimation caused most of the problems. The method still needs further experimentations, therefore applications aim to compare results when varying initial assumptions and to identify their pros and cons rather than provide actual results on the Italian population. Eventually a model extension, along with the first results of its application, is proposed using the Conway-Maxwell Poisson distribution Conway and Maxwell (1962), a flexible two parameters version of the Poisson distribution.
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Hivert, Valentin. "Analyse de la différenciation génétique à l'ère des nouvelles technologies de séquençage." Electronic Thesis or Diss., Montpellier, SupAgro, 2018. http://www.theses.fr/2018NSAM0061.
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L’avancée des technologies de séquençage et de génotypage à haut-débit permet la comparaison de patrons de polymorphisme à un très grand nombre de marqueurs génétiques. L'analyse de la différenciation des populations à une échelle génomique rend ainsi possible la recherche de régions génomiques impliquées dans l’adaptation locale des organismes à leur environnement. Dans cette thèse, nous avons suivi deux approches complémentaires pour caractériser la différenciation génétique à partir de données de génotypage à haut-débit. Dans un premier temps, nous avons développé un estimateur non-biaisé du paramètre FST pour des données de génotypage d’individus en mélange (Pool-seq). La construction de cet estimateur, dans un contexte d’analyse de variance, a nécessité de bien prendre en compte les différentes étapes de l’échantillonnage : des gènes dans le mélange d'individus et des lectures de séquençage parmi les gènes. Nous montrons qu’il surpasse les estimateurs utilisés jusqu'à présent. Dans un deuxième temps, nous avons développé une méthode d'analyse de la différenciation génétique à l'échelle du génome, dans le cadre d’un modèle bayésien hiérarchique, pour distinguer l'effet de la démographie de celui de la sélection. Pour cela, nous avons implémenté plusieurs extensions au modèle SelEstim, pour exploiter l'information de déséquilibre de liaison entre les marqueurs. Une première stratégie a consisté à analyser des données multialléliques, obtenues par le regroupement local de marqueurs SNPs en blocs d'haplotypes. Une stratégie alternative a consisté à intégrer un modèle de lissage prenant en compte la dépendance spatiale entre marqueurs adjacents. Cette approche repose sur l'analyse de données bialléliques, ce qui la rend applicable à la fois à des données de génotypage individuel et à des données Pool-seq. Nous discutons, sur la base de l'analyse de jeux de données simulées, des mérites relatifs de ces différentes approchesThe advent of high throughput sequencing and genotyping technologies allows the comparison of patterns of polymorphisms at a very large number of genetic markers. The analysis of genetic differentiation between populations at a whole-genome scale makes it possible to characterize genomic regions involved in the local adaptation of organisms to their environment. In this thesis, we followed two complementary approaches to characterize differentiation from high-throughput genotyping data. First, we developed an unbiased estimator of the parameter FST for individuals sequenced in pools (Pool-seq). Deriving this estimator, in an analysis-of-variance framework, required to properly account for the different sampling steps: individual genes from the pool, and sequence reads from these genes. We show that it outperforms previously proposed estimators. Second, we developed a method to analyze genetic differentiation at a whole-genome scale in a hierarchical bayesian framework, in order to untangle the effect of demography from that of selection. To this end, we implemented different extensions to the SelEstim model, aimed at leveraging the information from linkage disequilibrium between markers. A first approach consisted in analyzing multiallelic data derived from the local clustering of SNPs into haplotype blocks. An alternative strategy consisted in including a smoothing model, which accounts for the spatial dependency between neighboring markers. This strategy relies on the analysis of biallelic data, and can be used both with individual genotype data or Pool-seq data. We discuss the relative benefits of these different approaches, based on the analysis of simulated data sets
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Gao, Yong. "A Degradation-based Burn-in Optimization for Light Display Devices with Two-phase Degradation Patterns considering Warranty Durations and Measurement Errors." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1509109739168013.
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Wendling, Thierry. "Hierarchical mechanistic modelling of clinical pharmacokinetic data." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/hierarchical-mechanistic-modelling-of-clinical-pharmacokinetic-data(573652c9-d3fb-4233-bea7-7abd7ef48d4b).html.
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Pharmacokinetic and pharmacodynamic models can be applied to clinical study data using various modelling approaches depending on the aim of the analysis. In population pharmacokinetics for instance, simple compartmental models can be employed to describe concentration-time data, identify prognostic factors and interpolate within well-defined experimental conditions. The first objective of this thesis was to illustrate such a ‘semi-mechanistic’ pharmacokinetic modelling approach using mavoglurant as an example of a compound under clinical development. In particular, methods to accurately characterise complex oral pharmacokinetic profiles and evaluate the impact of absorption factors were investigated. When the purpose of the model-based analysis is to further extrapolate beyond the experimental conditions in order to guide the design of subsequent clinical trials, physiologically-based pharmacokinetic (PBPK) models are more valuable as they incorporate information not only on the drug but also on the system, i.e. on mammillary anatomy and physiology. The combination of such mechanistic models with statistical modelling techniques in order to analysis clinical data has been widely applied in toxicokinetics but has only recently received increasing interest in pharmacokinetics. This is probably because, due to the higher complexity of PBPK models compared to conventional pharmacokinetic models, additional efforts are required for adequate population data analysis. Hence, the second objective of this thesis was to explore methods to allow the application of PBPK models to clinical study data, such as the Bayesian approach or model order reduction techniques, and propose a general mechanistic modelling workflow for population data analysis. In pharmacodynamics, mechanistic modelling of clinical data is even less common than in pharmacokinetics. This is probably because our understanding of the interaction between therapeutic drugs and biological processes is limited and also because the types of data to analyse are often more complex than pharmacokinetic data. In oncology for instance, the most widely used clinical endpoint to evaluate the benefit of an experimental treatment is survival of patients. Survival data are typically censored due to logistic constraints associated with patient follow-up. Hence, the analysis of survival data requires specific statistical techniques. Longitudinal tumour size data have been increasingly used to assess treatment response for solid tumours. In particular, the survival prognostic value of measures derived from such data has been recently evaluated for various types of cancer although not for pancreatic cancer. The last objective of this thesis was therefore to investigate different modelling approaches to analyse survival data of pancreatic cancer patients treated with gemcitabine, and compare tumour burden measures with other patient clinical characteristics and established risk factors, in terms of predictive value for survival.