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Journal articles on the topic 'Hierarchical Bayesian Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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1

Dunson, David B. "Bayesian nonparametric hierarchical modeling." Biometrical Journal 51, no. 2 (April 2009): 273–84. http://dx.doi.org/10.1002/bimj.200800183.

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2

Kim, Yongku. "Hierarchical Bayesian modeling for soil moisture." Journal of the Korean Data And Information Science Society 30, no. 4 (July 31, 2019): 713–21. http://dx.doi.org/10.7465/jkdi.2019.30.4.713.

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3

Driver, Charles C., and Manuel C. Voelkle. "Hierarchical Bayesian continuous time dynamic modeling." Psychological Methods 23, no. 4 (December 2018): 774–99. http://dx.doi.org/10.1037/met0000168.

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4

Phillips, D. B., and A. F. M. Smith. "Bayesian Faces via Hierarchical Template Modeling." Journal of the American Statistical Association 89, no. 428 (December 1994): 1151–63. http://dx.doi.org/10.1080/01621459.1994.10476855.

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5

Chávez, Melisa E., Elena Villalobos, José L. Baroja, and Arturo Bouzas. "Hierarchical Bayesian modeling of intertemporal choice." Judgment and Decision Making 12, no. 1 (January 2017): 19–28. http://dx.doi.org/10.1017/s1930297500005210.

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AbstractThere is a growing interest in studying individual differences in choices that involve trading off reward amount and delay to delivery because such choices have been linked to involvement in risky behaviors, such as substance abuse. The most ubiquitous proposal in psychology is to model these choices assuming delayed rewards lose value following a hyperbolic function, which has one free parameter, named discounting rate. Consequently, a fundamental issue is the estimation of this parameter. The traditional approach estimates each individual’s discounting rate separately, which discards individual differences during modeling and ignores the statistical structure of the population. The present work adopted a different approximation to parameter estimation: each individual’s discounting rate is estimated considering the information provided by all subjects, using state-of-the-art Bayesian inference techniques. Our goal was to evaluate whether individual discounting rates come from one or more subpopulations, using Mazur’s (1987) hyperbolic function. Twelve hundred eighty-four subjects answered the Intertemporal Choice Task developed by Kirby, Petry and Bickel (1999). The modeling techniques employed permitted the identification of subjects who produced random, careless responses, and who were discarded from further analysis. Results showed that one-mixture hierarchical distribution that uses the information provided by all subjects suffices to model individual differences in delay discounting, suggesting psychological variability resides along a continuum rather than in discrete clusters. This different approach to parameter estimation has the potential to contribute to the understanding and prediction of decision making in various real-world situations where immediacy is constantly in conflict with magnitude.
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6

Mahdi, Esam, Sana Alshamari, Maryam Khashabi, and Alya Alkorbi. "Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction." Journal of Applied Mathematics 2021 (September 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/8003952.

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Over the past few years, hierarchical Bayesian models have been extensively used for modeling the joint spatial and temporal dependence of big spatio-temporal data which commonly involves a large number of missing observations. This article represented, assessed, and compared some recently proposed Bayesian and non-Bayesian models for predicting the daily average particulate matter with a diameter of less than 10 (PM10) measured in Qatar during the years 2016–2019. The disaggregating technique with a Markov chain Monte Carlo method with Gibbs sampler are used to handle the missing data. Based on the obtained results, we conclude that the Gaussian predictive processes with autoregressive terms of the latent underlying space-time process model is the best, compared with the Bayesian Gaussian processes and non-Bayesian generalized additive models.
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7

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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8

Sawyer, Robert, Jonathan Rowe, Roger Azevedo, and James Lester. "Modeling Player Engagement with Bayesian Hierarchical Models." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (September 25, 2018): 257–63. http://dx.doi.org/10.1609/aiide.v14i1.13048.

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Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.
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9

Steffey, Duane. "Hierarchical bayesian modeling with elicited prior information." Communications in Statistics - Theory and Methods 21, no. 3 (January 1992): 799–821. http://dx.doi.org/10.1080/03610929208830816.

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10

Mardia, Kanti V., Vysaul B. Nyirongo, Christopher J. Fallaize, Stuart Barber, and Richard M. Jackson. "Hierarchical Bayesian Modeling of Pharmacophores in Bioinformatics." Biometrics 67, no. 2 (July 9, 2010): 611–19. http://dx.doi.org/10.1111/j.1541-0420.2010.01460.x.

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11

Berliner, Mark. "Hierarchical Bayesian modeling in the environmental sciences." Allgemeines Statistisches Archiv 84, no. 2 (July 6, 2000): 141–53. http://dx.doi.org/10.1007/s101820050013.

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12

Berlowitz, Dan R., Cindy L. Christiansen, Gary H. Brandeis, Arlene S. Ash, Boris Kader, John N. Morris, and Mark A. Moskowitz. "Profiling Nursing Homes Using Bayesian Hierarchical Modeling." Journal of the American Geriatrics Society 50, no. 6 (June 2002): 1126–30. http://dx.doi.org/10.1046/j.1532-5415.2002.50272.x.

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13

Kaizer, Alexander M., Joseph S. Koopmeiners, and Brian P. Hobbs. "Bayesian hierarchical modeling based on multisource exchangeability." Biostatistics 19, no. 2 (July 6, 2017): 169–84. http://dx.doi.org/10.1093/biostatistics/kxx031.

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14

Chen, Jie, Jinglin Zhong, and Lei Nie. "Bayesian hierarchical modeling of drug stability data." Statistics in Medicine 27, no. 13 (2008): 2361–80. http://dx.doi.org/10.1002/sim.3220.

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15

Broccardo, M., A. Mignan, S. Wiemer, B. Stojadinovic, and D. Giardini. "Hierarchical Bayesian Modeling of Fluid‐Induced Seismicity." Geophysical Research Letters 44, no. 22 (November 23, 2017): 11,357–11,367. http://dx.doi.org/10.1002/2017gl075251.

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16

Alaba, Oluwayemisi Oyeronke, and Chidinma Godwin. "Bayesian hierarchical modeling of infant mortality in Nigeria." Global Journal of Pure and Applied Sciences 25, no. 2 (September 6, 2019): 175–83. http://dx.doi.org/10.4314/gjpas.v25i2.7.

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Infant mortality and its risk factors in Nigeria was investigated using Bayesian hierarchical modeling. The hierarchical nature of the problem was examined to detect the within and between groups (states and regions) variations in infant deaths. The effect of individual level variables on the risk of a child dying before the age of one was determined using data collected from the fifth round Multiple Indicator Survey (MICS5, 2016-2017). Infants in Northern Nigeria had a higher risk of dying than others, especially in North West, while South West had the lowest risk of infant deaths. Ten percent of the variations in infant deaths was explained by differences between states while differences between regions explained only seven percent of the variations. Also, factors such as urban place of residence, mothers with secondary and tertiary education, first birth and birth interval above 2 years were associated with a decreased risk of infant deaths. Male infants, birth interval of less than 2 years, mothers with primary and no education, teenage mothers and mothers that gave birth at age 35 years and above were associated with a higher risk of infant mortality.
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17

Zarezadeh, Z., and G. Costantini. "Quantum-state diffusion: Application to Bayesian hierarchical modeling." Physica A: Statistical Mechanics and its Applications 584 (December 2021): 126382. http://dx.doi.org/10.1016/j.physa.2021.126382.

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18

Alexander, Neal. "Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 2 (March 14, 2011): 512–13. http://dx.doi.org/10.1111/j.1467-985x.2010.00681_11.x.

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19

Wikle, Christopher K., Ralph F. Milliff, Doug Nychka, and L. Mark Berliner. "Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds." Journal of the American Statistical Association 96, no. 454 (June 2001): 382–97. http://dx.doi.org/10.1198/016214501753168109.

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20

Jensen, Shane T., Blakeley B. McShane, and Abraham J. Wyner. "Hierarchical Bayesian modeling of hitting performance in baseball." Bayesian Analysis 4, no. 4 (December 2009): 631–52. http://dx.doi.org/10.1214/09-ba424.

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21

Chi, Yueh-Yun, Joseph G. Ibrahim, Anika Bissahoyo, and David W. Threadgill. "Bayesian Hierarchical Modeling for Time Course Microarray Experiments." Biometrics 63, no. 2 (December 7, 2006): 496–504. http://dx.doi.org/10.1111/j.1541-0420.2006.00689.x.

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22

Elsner, James B., and Thomas H. Jagger. "A Hierarchical Bayesian Approach to Seasonal Hurricane Modeling." Journal of Climate 17, no. 14 (July 2004): 2813–27. http://dx.doi.org/10.1175/1520-0442(2004)017<2813:ahbats>2.0.co;2.

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23

McCormick, Tyler H., Cynthia Rudin, and David Madigan. "Bayesian hierarchical rule modeling for predicting medical conditions." Annals of Applied Statistics 6, no. 2 (June 2012): 652–68. http://dx.doi.org/10.1214/11-aoas522.

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24

Sosa, Juan, and Jeimy-Paola Aristizabal. "Some Developments in Bayesian Hierarchical Linear Regression Modeling." Revista Colombiana de Estadística 45, no. 2 (July 14, 2022): 231–55. http://dx.doi.org/10.15446/rce.v45n2.98988.

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Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).
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25

Natvig, Bent, and Ingunn Fride Tvete. "Bayesian Hierarchical Space–time Modeling of Earthquake Data." Methodology and Computing in Applied Probability 9, no. 1 (January 23, 2007): 89–114. http://dx.doi.org/10.1007/s11009-006-9008-0.

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26

Jiang, Xiaomo, and Sankaran Mahadevan. "Bayesian hierarchical uncertainty quantification by structural equation modeling." International Journal for Numerical Methods in Engineering 80, no. 6‒7 (November 5, 2009): 717–37. http://dx.doi.org/10.1002/nme.2550.

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27

Jiao, Yan, Laura Rogers-Bennett, Ian Taniguchi, John Butler, and Paul Crone. "Incorporating temporal variation in the growth of red abalone (Haliotis rufescens) using hierarchical Bayesian growth models." Canadian Journal of Fisheries and Aquatic Sciences 67, no. 4 (April 2010): 730–42. http://dx.doi.org/10.1139/f10-019.

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Many marine species exhibit temporal variation in individual growth. Yearly variation in growth has been identified for red abalone ( Haliotis rufescens ) in southern California, USA, but has not been previously incorporated into growth models. In this study, Bayesian hierarchical models were developed to describe variability in growth rates for the Johnsons Lee red abalone population. Although the Bayesian hierarchical modeling estimates are close to estimates of the nonhierarchical highly parameterized model that assigns an estimate of parameters to each data period when the sample sizes are high, the hyperparameters in the hierarchical model are more useful in incorporating the temporal variability into the stock assessment. By ignoring temporal variability, confidence intervals of the estimates of growth can be unrealistically narrow, possibly leading to bias when these models are used for developing biological reference points such as F0.1, Fmax, or Fx%. The use of a Bayesian hierarchical approach is generally suggested for future growth modeling and for per-recruitment models that include growth when determining precautionary management decisions.
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28

Song, Mingming, Iman Behmanesh, Babak Moaveni, and Costas Papadimitriou. "Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview." Sensors 20, no. 14 (July 11, 2020): 3874. http://dx.doi.org/10.3390/s20143874.

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Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided.
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29

DiMaggio, Charles, Sandro Galea, and David Abramson. "Analyzing Postdisaster Surveillance Data: The Effect of the Statistical Method." Disaster Medicine and Public Health Preparedness 2, no. 2 (June 2008): 119–26. http://dx.doi.org/10.1097/dmp.0b013e31816c7475.

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ABSTRACTData from existing administrative databases and ongoing surveys or surveillance methods may prove indispensable after mass traumas as a way of providing information that may be useful to emergency planners and practitioners. The analytic approach, however, may affect exposure prevalence estimates and measures of association. We compare Bayesian hierarchical modeling methods to standard survey analytic techniques for survey data collected in the aftermath of a terrorist attack. Estimates for the prevalence of exposure to the terrorist attacks of September 11, 2001, varied by the method chosen. Bayesian hierarchical modeling returned the lowest estimate for exposure prevalence with a credible interval spanning nearly 3 times the range of the confidence intervals (CIs) associated with both unadjusted and survey procedures. Bayesian hierarchical modeling also returned a smaller point estimate for measures of association, although in this instance the credible interval was tighter than that obtained through survey procedures. Bayesian approaches allow a consideration of preexisting assumptions about survey data, and may offer potential advantages, particularly in the uncertain environment of postterrorism and disaster settings. Additional comparative analyses of existing data are necessary to guide our ability to use these techniques in future incidents. (Disaster Med Public Health Preparedness. 2008;2:119–126)
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30

Wang, Aobo, and David C. Wheeler. "Catchment Area Analysis Using Bayesian Regression Modeling." Cancer Informatics 14s2 (January 2015): CIN.S17297. http://dx.doi.org/10.4137/cin.s17297.

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A catchment area (CA) is the geographic area and population from which a cancer center draws patients. Defining a CA allows a cancer center to describe its primary patient population and assess how well it meets the needs of cancer patients within the CA. A CA definition is required for cancer centers applying for National Cancer Institute (NCI)-designated cancer center status. In this research, we constructed both diagnosis and diagnosis/treatment CAs for the Massey Cancer Center (MCC) at Virginia Commonwealth University. We constructed diagnosis CAs for all cancers based on Virginia state cancer registry data and Bayesian hierarchical logistic regression models. We constructed a diagnosis/treatment CA using billing data from MCC and a Bayesian hierarchical Poisson regression model. To define CAs, we used exceedance probabilities for county random effects to assess unusual spatial clustering of patients diagnosed or treated at MCC after adjusting for important demographic covariates. We used the MCC CAs to compare patient characteristics inside and outside the CAs. Among cancer patients living within the MCC CA, patients diagnosed at MCC were more likely to be minority, female, uninsured, or on Medicaid.
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31

Kadoishi, Seiya, and Hironobu Kawamura. "Control Charts Based on Hierarchical Bayesian Modeling." Total Quality Science 5, no. 2 (January 25, 2020): 72–80. http://dx.doi.org/10.17929/tqs.5.72.

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32

Jia, Xinyu, Omid Sedehi, Costas Papadimitriou, Lambros S. Katafygiotis, and Babak Moaveni. "Nonlinear model updating through a hierarchical Bayesian modeling framework." Computer Methods in Applied Mechanics and Engineering 392 (March 2022): 114646. http://dx.doi.org/10.1016/j.cma.2022.114646.

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33

Chen, Jiaxun, Athanasios C. Micheas, and Scott H. Holan. "Hierarchical Bayesian modeling of spatio-temporal area-interaction processes." Computational Statistics & Data Analysis 167 (March 2022): 107349. http://dx.doi.org/10.1016/j.csda.2021.107349.

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34

Geyer, Sebastian, Iason Papaioannou, and Daniel Straub. "Bayesian analysis of hierarchical random fields for material modeling." Probabilistic Engineering Mechanics 66 (October 2021): 103167. http://dx.doi.org/10.1016/j.probengmech.2021.103167.

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35

Neil, Martin, and Norman Fenton. "Bayesian Hypothesis Testing and Hierarchical Modeling of Ivermectin Effectiveness." American Journal of Therapeutics 28, no. 5 (September 2021): e576-e579. http://dx.doi.org/10.1097/mjt.0000000000001450.

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36

Theiss, Justin, and Michael Silver. "Modeling attention during visual search with hierarchical Bayesian inference." Journal of Vision 19, no. 10 (September 6, 2019): 107a. http://dx.doi.org/10.1167/19.10.107a.

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37

Pruteanu-Malinici, Iulian, Lu Ren, John Paisley, Eric Wang, and Lawrence Carin. "Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 6 (June 2010): 996–1011. http://dx.doi.org/10.1109/tpami.2009.125.

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38

Vandenhende, F., D. Renard, Y. Nie, A. Kumar, J. Miller, J. Tauscher, J. Witcher, Y. Zhou, and D. F. Wong. "Bayesian Hierarchical Modeling of Receptor Occupancy in PET Trials." Journal of Biopharmaceutical Statistics 18, no. 2 (March 7, 2008): 256–72. http://dx.doi.org/10.1080/10543400701697158.

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39

Lian, Heng. "Sparse Bayesian hierarchical modeling of high-dimensional clustering problems." Journal of Multivariate Analysis 101, no. 7 (August 2010): 1728–37. http://dx.doi.org/10.1016/j.jmva.2010.03.009.

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40

Jiang, Xiaomo, and Sankaran Mahadevan. "Bayesian structural equation modeling method for hierarchical model validation." Reliability Engineering & System Safety 94, no. 4 (April 2009): 796–809. http://dx.doi.org/10.1016/j.ress.2008.08.008.

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41

Lee, Michael D. "How cognitive modeling can benefit from hierarchical Bayesian models." Journal of Mathematical Psychology 55, no. 1 (February 2011): 1–7. http://dx.doi.org/10.1016/j.jmp.2010.08.013.

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42

Molloy, M. Fiona, Giwon Bahg, Xiangrui Li, Mark Steyvers, Zhong-Lin Lu, and Brandon M. Turner. "Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data." Computational Brain & Behavior 1, no. 2 (June 2018): 184–213. http://dx.doi.org/10.1007/s42113-018-0013-5.

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43

Johnson, Matthew S., and Sandip Sinharay. "Calibration of Polytomous Item Families Using Bayesian Hierarchical Modeling." Applied Psychological Measurement 29, no. 5 (September 2005): 369–400. http://dx.doi.org/10.1177/0146621605276675.

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44

Yu, Yun, Scott Steinschneider, and David A. Reckhow. "Evaluation of Environmental Degradation Kinetics Using Hierarchical Bayesian Modeling." Journal of Environmental Engineering 141, no. 12 (December 2015): 06015008. http://dx.doi.org/10.1061/(asce)ee.1943-7870.0000997.

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45

Chen, Geng, and Sheng Luo. "Bayesian hierarchical joint modeling using skew-normal/independent distributions." Communications in Statistics - Simulation and Computation 47, no. 5 (June 28, 2017): 1420–38. http://dx.doi.org/10.1080/03610918.2017.1315730.

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46

Brynjarsdóttir, Jenný, and L. Mark Berliner. "Bayesian hierarchical modeling for temperature reconstruction from geothermal data." Annals of Applied Statistics 5, no. 2B (June 2011): 1328–59. http://dx.doi.org/10.1214/10-aoas452.

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47

MCBRIDE, S., R. WILLIAMS, and J. CREASON. "Bayesian hierarchical modeling of personal exposure to particulate matter." Atmospheric Environment 41, no. 29 (September 2007): 6143–55. http://dx.doi.org/10.1016/j.atmosenv.2007.04.005.

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48

Johnson, Matthew S., and Sandip Sinharay. "CALIBRATION OF POLYTOMOUS ITEM FAMILIES USING BAYESIAN HIERARCHICAL MODELING." ETS Research Report Series 2003, no. 2 (December 2003): i—30. http://dx.doi.org/10.1002/j.2333-8504.2003.tb01915.x.

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49

Reza Najafi, Mohammad, and Hamid Moradkhani. "Analysis of runoff extremes using spatial hierarchical Bayesian modeling." Water Resources Research 49, no. 10 (October 2013): 6656–70. http://dx.doi.org/10.1002/wrcr.20381.

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50

Dunn, Laurel N., Ioanna Kavvada, Mathilde D. Badoual, and Scott J. Moura. "Bayesian hierarchical methods for modeling electrical grid component failures." Electric Power Systems Research 189 (December 2020): 106789. http://dx.doi.org/10.1016/j.epsr.2020.106789.

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