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Добірка наукової літератури з теми "Bayesian hierarchical spatiotemporal models"

Автор: Grafiati

Опубліковано: 13 липня 2022

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Статті в журналах з теми "Bayesian hierarchical spatiotemporal models"

Alghamdi, Taghreed, Khalid Elgazzar, and Taysseer Sharaf. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling." Future Internet 13, no. 9 (August 30, 2021): 225. http://dx.doi.org/10.3390/fi13090225.

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Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

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Cosandey-Godin, Aurelie, Elias Teixeira Krainski, Boris Worm, and Joanna Mills Flemming. "Applying Bayesian spatiotemporal models to fisheries bycatch in the Canadian Arctic." Canadian Journal of Fisheries and Aquatic Sciences 72, no. 2 (February 2015): 186–97. http://dx.doi.org/10.1139/cjfas-2014-0159.

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Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable species such as sharks, is a major challenge for contemporary fisheries management. Here we establish integrated nested Laplace approximations (INLA) and stochastic partial differential equations (SPDE) as two powerful tools for modelling patterns of bycatch through time and space. These novel, computationally fast approaches are applied to fit zero-inflated hierarchical spatiotemporal models to Greenland shark (Somniosus microcephalus) bycatch data from the Baffin Bay Greenland halibut (Reinhardtius hippoglossoides) gillnet fishery. Results indicate that Greenland shark bycatch is clustered in space and time, varies significantly from year to year, and there are both tractable factors (number of gillnet panels, total Greenland halibut catch) and physical features (bathymetry) leading to the high incidence of Greenland shark bycatch. Bycatch risk could be reduced by limiting access to spatiotemporal hotspots or by establishing a maximum number of panels per haul. Our method explicitly models the spatiotemporal correlation structure inherent in bycatch data at a very reasonable computational cost, such that the forecasting of bycatch patterns and simulating conservation strategies becomes more accessible.

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Blangiardo, Marta, Areti Boulieri, Peter Diggle, Frédéric B. Piel, Gavin Shaddick, and Paul Elliott. "Advances in spatiotemporal models for non-communicable disease surveillance." International Journal of Epidemiology 49, Supplement_1 (April 1, 2020): i26—i37. http://dx.doi.org/10.1093/ije/dyz181.

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Abstract Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.

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Bi, Rujia, Yan Jiao, Can Zhou, and Eric Hallerman. "A Bayesian spatiotemporal approach to inform management unit appropriateness." Canadian Journal of Fisheries and Aquatic Sciences 76, no. 2 (February 2019): 217–37. http://dx.doi.org/10.1139/cjfas-2017-0526.

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One prerequisite for sustainable fisheries management is to match management actions with biological processes. Stocks are fundamental units for fisheries management. Understanding the spatial structure of fish stocks is critical for conducting defensible stock assessments, applying efficient management strategies, and ensuring the sustainability of fish stocks. Yellow perch (Perca flavescens) is an important fishery in the Great Lakes. The appropriateness of its management units (MUs) has been identified as of high concern by the Great Lakes Fisheries Commission. Here we established integrated nested Laplace approximations and stochastic partial differential equations as two powerful tools for modeling spatiotemporal patterns of fish relative biomass. These fast computational approaches were applied to fit a Bayesian hierarchical hurdle model to occurrence and positive mass of yellow perch caught in gill-net surveys. Yellow perch relative biomass index has clear temporal variation and spatial heterogeneity, with the two middle MUs for yellow perch within Lake Erie merging together. The method explicitly models the spatiotemporal correlation structure inherent in biomass survey data at a reasonable computational cost, and the estimated spatiotemporal correlation informs stock structure.

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Neelon, Brian, Howard H. Chang, Qiang Ling, and Nicole S. Hastings. "Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits." Statistical Methods in Medical Research 25, no. 6 (September 30, 2016): 2558–76. http://dx.doi.org/10.1177/0962280214527079.

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Motivated by a study exploring spatiotemporal trends in emergency department use, we develop a class of two-part hurdle models for the analysis of zero-inflated areal count data. The models consist of two components—one for the probability of any emergency department use and one for the number of emergency department visits given use. Through a hierarchical structure, the models incorporate both patient- and region-level predictors, as well as spatially and temporally correlated random effects for each model component. The random effects are assigned multivariate conditionally autoregressive priors, which induce dependence between the components and provide spatial and temporal smoothing across adjacent spatial units and time periods, resulting in improved inferences. To accommodate potential overdispersion, we consider a range of parametric specifications for the positive counts, including truncated negative binomial and generalized Poisson distributions. We adopt a Bayesian inferential approach, and posterior computation is handled conveniently within standard Bayesian software. Our results indicate that the negative binomial and generalized Poisson hurdle models vastly outperform the Poisson hurdle model, demonstrating that overdispersed hurdle models provide a useful approach to analyzing zero-inflated spatiotemporal data.

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Song, Chao, Yaqian He, Yanchen Bo, Jinfeng Wang, Zhoupeng Ren, and Huibin Yang. "Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models." International Journal of Environmental Research and Public Health 15, no. 7 (July 12, 2018): 1476. http://dx.doi.org/10.3390/ijerph15071476.

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Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health.

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Gopalan, Giri, Birgir Hrafnkelsson, Guðfinna Aðalgeirsdóttir, Alexander H. Jarosch, and Finnur Pálsson. "A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions." Cryosphere 12, no. 7 (July 11, 2018): 2229–48. http://dx.doi.org/10.5194/tc-12-2229-2018.

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Abstract. Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatiotemporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error-correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier.

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Paradinas, I., D. Conesa, A. López-Quílez, A. Esteban, LM Martín López, JM Bellido, and MG Pennino. "Assessing the spatiotemporal persistence of fish distributions: a case study on two red mullet species (Mullus surmuletus and M. barbatus) in the western Mediterranean." Marine Ecology Progress Series 644 (June 25, 2020): 173–85. http://dx.doi.org/10.3354/meps13366.

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Understanding the spatiotemporal persistence of fish distributions is key to defining fish hotspots and effective fisheries-restricted areas (FRAs). Hierarchical Bayesian spatiotemporal models provide an excellent framework to understand these distributions, as they can accommodate different spatiotemporal behaviour in the data, primarily due to their flexibility. The aim of this research was to characterize the fundamental behavioural patterns of fish as persistent, opportunistic or progressive by comparing different spatiotemporal model structures in order to provide better information for marine spatial planning. To illustrate this method, the spatiotemporal distributions of 2 sympatric Mullidae species, the striped red mullet Mullus surmuletus and the red mullet M. barbatus, were analysed. The occurrence of each species, its conditional-to-presence abundance and median length were analysed using Mediterranean trawl survey data from the western Mediterranean between 2000 and 2016. Results demonstrate that there are various common hotspots of both species distributed along the Iberian coast. The convenient persistent spatiotemporal distribution of these hotspots facilitates the configuration of a network of connected FRAs for red mullets in the study area.

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Baer, Daniel R., Andrew B. Lawson, and Jane E. Joseph. "Joint space–time Bayesian disease mapping via quantification of disease risk association." Statistical Methods in Medical Research 30, no. 1 (January 2021): 35–61. http://dx.doi.org/10.1177/0962280220938975.

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Alzheimer’s disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer’s disease risk in order to preclude its inception in patients. Characterizing Alzheimer’s disease risk can be accomplished at the population-level by the space–time modeling of Alzheimer’s disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer’s disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer’s disease risk over space and time. From an application of these models to real-world Alzheimer’s disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer’s disease risk.

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Song, Li, Yang Li, Wei (David) Fan, and Peijie Wu. "Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: Insights from different hierarchical Bayesian random-effects models." Analytic Methods in Accident Research 28 (December 2020): 100137. http://dx.doi.org/10.1016/j.amar.2020.100137.

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Дисертації з теми "Bayesian hierarchical spatiotemporal models"

Ling, Yuheng. "Corsican housing market analysis : Applications of bayesian hierarchical model." Thesis, Corte, 2020. http://www.theses.fr/2020CORT0011.

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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 publics
This 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

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Al-Kaabawi, Zainab A. A. "Bayesian hierarchical models for linear networks." Thesis, University of Plymouth, 2018. http://hdl.handle.net/10026.1/12829.

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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.

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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.

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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.

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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.

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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.

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Lin, Xiaoyan. "Bayesian hierarchical models for the recognition-memory experiments." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6047.

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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.

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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.

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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/.

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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.

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Книги з теми "Bayesian hierarchical spatiotemporal models"

Congdon, P. Applied Bayesian hierarchical methods. Boca Raton: Chapman & Hall/CRC, 2010.

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Congdon, P. Applied Bayesian hierarchical methods. Boca Raton: Chapman & Hall/CRC, 2010.

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Thériault, Marc-Erick. Bayesian hierarchical models for mapping lung cancer mortality in Ontario. Ottawa: National Library of Canada, 2000.

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Sun, Li. Bayesian estimation procedures for one and two-way hierarchical models. Toronto: [s.n.], 1992.

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Vanem, Erik. Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30253-4.

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Congdon, Peter D. Bayesian Hierarchical Models. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429113352.

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Kruschke, John K., and Wolf Vanpaemel. Bayesian Estimation in Hierarchical Models. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.13.

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Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn are in higher-level organizations. Hierarchical models have parameters that meaningfully describe the data at their multiple levels and connect information within and across levels. Bayesian methods are very flexible and straightforward for estimating parameters of complex hierarchical models (and simpler models too). We provide an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples. One example considers baseball batting averages of individual players grouped by fielding position. A second example uses a hierarchical extension of a cognitive process model to examine individual differences in attention allocation of people who have eating disorders. We conclude by discussing Bayesian model comparison as a case of hierarchical modeling.

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Bayesian Disease Mapping Hierarchical Modeling In Spatial Epidemiology. Taylor & Francis Inc, 2013.

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Bayesian Random Effect and Other Hierarchical Models: An Applied Perspective. Chapman & Hall/CRC, 2009.

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Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height. Springer, 2013.

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Hooten, Mevin B., and Trevor J. Hefley. "Hierarchical Models." In Bringing Bayesian Models to Life, 221–38. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653-19.

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Bottolo, Leonardo, and Petros Dellaportas. "Bayesian Hierarchical Mixture Models." In Statistical Analysis for High-Dimensional Data, 91–103. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27099-9_5.

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Rosner, Gary L., Purushottam W. Laud, and Wesley O. Johnson. "Hierarchical Models and Longitudinal Data." In Bayesian Thinking in Biostatistics, 427–80. First edition. | Boca Raton: CRC Press, 2021.: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781439800102-14.

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Berliner, L. Mark. "Hierarchical Bayesian Time Series Models." In Maximum Entropy and Bayesian Methods, 15–22. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5430-7_3.

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Escobar, Michael D., and Mike West. "Computing Nonparametric Hierarchical Models." In Practical Nonparametric and Semiparametric Bayesian Statistics, 1–22. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1732-9_1.

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Earnest, Arul, Susanna M. Cramb, and Nicole M. White. "Disease Mapping Using Bayesian Hierarchical Models." In Case Studies in Bayesian Statistical Modelling and Analysis, 221–39. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118394472.ch13.

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Pennello, Gene, and Mark Rothmann. "Bayesian Subgroup Analysis with Hierarchical Models." In Biopharmaceutical Applied Statistics Symposium, 175–92. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7826-2_10.

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Lynch, Scott M. "Introduction to Hierarchical Models." In Introduction to Applied Bayesian Statistics and Estimation for Social Scientists, 231–69. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-71265-9_9.

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Heard, Nick. "Graphical Modelling and Hierarchical Models." In An Introduction to Bayesian Inference, Methods and Computation, 23–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82808-0_3.

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Montgomery, Alan L. "Hierarchical Bayes Models for Micro-Marketing Strategies." In Case Studies in Bayesian Statistics, 95–153. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2290-3_3.

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Alghamdi, Taghreed, Khalid Elgazzar, and Taysseer Sharaf. "Spatiotemporal Prediction Using Hierarchical Bayesian Modeling." In 2020 International Conference on Communications, Signal Processing, and their Applications (ICCSPA). IEEE, 2021. http://dx.doi.org/10.1109/iccspa49915.2021.9385767.

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Bundschus, Markus, Shipeng Yu, Volker Tresp, Achim Rettinger, Mathaeus Dejori, and Hans-Peter Kriegel. "Hierarchical Bayesian Models for Collaborative Tagging Systems." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.121.

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Wang, Xiaogang, Xiaoxu Ma, and Eric Grimson. "Unsupervised Activity Perception by Hierarchical Bayesian Models." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383072.

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Hendrix, Philip, Ya'akov Gal, and Avi Pfeffer. "Using Hierarchical Bayesian Models to Learn about Reputation." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.349.

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Yalamanchili, Pavan, and Tarek M. Taha. "Multicore cluster implementations of hierarchical Bayesian cortical models." In 2009 12th International Conference on Computer and Information Technology (ICCIT). IEEE, 2009. http://dx.doi.org/10.1109/iccit.2009.5407276.

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Kang, Xin, and Fuji Ren. "Understanding Blog author's emotions with hierarchical Bayesian models." In 2016 IEEE 13th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2016. http://dx.doi.org/10.1109/icnsc.2016.7479037.

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Vono, Maxime, Nicolas Dobigeon, and Pierre Chainais. "Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682982.

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Peeling, Paul, A. Taylan Cemgil, and Simon Godsill. "Bayesian hierarchical models and inference for musical audio processing." In 2008 3rd International Symposium on Wireless Pervasive Computing (ISWPC). IEEE, 2008. http://dx.doi.org/10.1109/iswpc.2008.4556214.

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Ballesteros, G. C., P. Angelikopoulos, C. Papadimitriou, and P. Koumoutsakos. "Bayesian Hierarchical Models for Uncertainty Quantification in Structural Dynamics." In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.162.

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Alghamdi, Taghreed, Khalid Elgazzar, Sifatul Mostafi, and James Ng. "Improving Spatiotemporal Traffic Prediction in Adversary Weather Conditions Using Hierarchical Bayesian State Space Modeling." In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021. http://dx.doi.org/10.1109/itsc48978.2021.9565005.

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Wikle, Christopher K., Mark Berliner, and Ralph F. Milliff. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada613068.

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Berliner, Mark, Emanuele Di Lorenzo, Ralph F. Milliff, and Christopher K. Wikle. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533499.

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Wikle, Christopher K., Mark Berliner, Emanuele Di Lorenzo, and Ralph F. Milliff. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533987.

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Milliff, Ralph F., Mark Berliner, Emanuele D. Lorenzo, and Christopher K. Wikle. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada534098.

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Milliff, Ralph F., Christopher K. Wikle, L. M. Berliner, and Emanuele Di Lorenzo. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada597815.

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Milliff, Ralph F., Mark Berliner, Emanuele Di Lorenzo, and Christopher K. Wikle. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada573354.

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Milliff, Ralph F., Mark Berliner, and Christopher K. Wikle. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630916.

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Berliner, Mark, Ralph F. Milliff, and Christopher K. Wikle. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630937.

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Milliff, Ralph F., Christopher K. Wikle, L. M. Berliner, and Emanuele Di Lorenzo. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada557010.

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