Journal articles: 'Bayesian hierarchical model'

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Zhuang, Haoxin, Liqun Diao, and Grace Y. Yi. "A Bayesian hierarchical copula model." Electronic Journal of Statistics 14, no. 2 (2020): 4457–88. http://dx.doi.org/10.1214/20-ejs1784.

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Jang, Eun Jin, and Dal Ho Kim. "Bayesian hierarchical model for publication bias." Journal of the Korean Data And Information Science Society 30, no. 1 (January 31, 2019): 1–10. http://dx.doi.org/10.7465/jkdi.2019.30.1.1.

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Mitra, Riten, Ryan Gill, Sinjini Sikdar, and Susmita Datta. "Bayesian hierarchical model for protein identifications." Journal of Applied Statistics 46, no. 1 (March 25, 2018): 30–46. http://dx.doi.org/10.1080/02664763.2018.1454893.

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Chu, Yiyi, and Ying Yuan. "A Bayesian basket trial design using a calibrated Bayesian hierarchical model." Clinical Trials 15, no. 2 (March 2, 2018): 149–58. http://dx.doi.org/10.1177/1740774518755122.

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Background: The basket trial evaluates the treatment effect of a targeted therapy in patients with the same genetic or molecular aberration, regardless of their cancer types. Bayesian hierarchical modeling has been proposed to adaptively borrow information across cancer types to improve the statistical power of basket trials. Although conceptually attractive, research has shown that Bayesian hierarchical models cannot appropriately determine the degree of information borrowing and may lead to substantially inflated type I error rates. Methods: We propose a novel calibrated Bayesian hierarchical model approach to evaluate the treatment effect in basket trials. In our approach, the shrinkage parameter that controls information borrowing is not regarded as an unknown parameter. Instead, it is defined as a function of a similarity measure of the treatment effect across tumor subgroups. The key is that the function is calibrated using simulation such that information is strongly borrowed across subgroups if their treatment effects are similar and barely borrowed if the treatment effects are heterogeneous. Results: The simulation study shows that our method has substantially better controlled type I error rates than the Bayesian hierarchical model. In some scenarios, for example, when the true response rate is between the null and alternative, the type I error rate of the proposed method can be inflated from 10% up to 20%, but is still better than that of the Bayesian hierarchical model. Limitation: The proposed design assumes a binary endpoint. Extension of the proposed design to ordinal and time-to-event endpoints is worthy of further investigation. Conclusion: The calibrated Bayesian hierarchical model provides a practical approach to design basket trials with more flexibility and better controlled type I error rates than the Bayesian hierarchical model. The software for implementing the proposed design is available at http://odin.mdacc.tmc.edu/~yyuan/index_code.html

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Et.al, Md Azman Shahadan. "Bayesian Hierarchical Growth Model for Experimental Data on the Effectiveness of an Incentive-Based Weight Reduction Method." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 1036–47. http://dx.doi.org/10.17762/turcomat.v12i3.840.

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The objective of this current research is to model the experimental data on the effectiveness of an incentive-based weight reduction method by using Bayesian hierarchical growth models. Three Bayesian hierarchical growth models are proposed, namely parametric Bayesian hierarchical growth model with correlated intercept and slope random effects model, parametric Bayesian hierarchical growth model with no correlated intercept and slope random effects model and semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The data is obtained from forty eight (48) students who had participated in an experiment on weight reduction method. The students were divided equally into two groups: single and pair groups. The experiment was carried out over the period of three months with a weight reading session for every two weeks. At the end of the study, we had six repeated measures of each student’s weight in kg and some measures of covariates and factors. Our results showed that the best model for the above data based on the Bayesian fit indexes and the models’ flexibility is the semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The results of the semi-parametric model showed that the ‘growth’ or reduction rates of the weight reduction experiment relate to the students’ gender, height in cm, experimental group (single or pair) and time in term of weeks.

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Kwon, Hyun-Han, Jin-Young Kim, Oon-Ki Kim, and Jeong-Ju Lee. "A Development of Regional Frequency Model Based on Hierarchical Bayesian Model." Journal of Korea Water Resources Association 46, no. 1 (January 31, 2013): 13–24. http://dx.doi.org/10.3741/jkwra.2013.46.1.13.

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Liu, Mei, Wen Qian Shi, and Jun Lu. "Predict River Water Quality Based on Bayesian Hierarchical Model." Applied Mechanics and Materials 409-410 (September 2013): 208–13. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.208.

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According to many uncertain problems of river eutrophication, a Bayesian hierarchical model was established to predict water quality. We applied the hierarchical method to assess river water quality in an agricultural watershed in eastern China. The procedure followed was developing a hierarchical model relating eutrophication response - the level of chlorophyll (Chla). Through Principal Component Analysis (PCA), five factors strong related with Chla were selected to establish Bayesian hierarchical model to predict the water quality. Results showed that Bayesian hierarchical model was very realistic. Furthermore, in Bayesian perspective, predictions expressed as probabilities, rather than a single value, involving more uncertainty information, can be essential to environmental management and decision-making.

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Salakpi, Edward E., Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani. "A dynamic hierarchical Bayesian approach for forecasting vegetation condition." Natural Hazards and Earth System Sciences 22, no. 8 (August 23, 2022): 2725–49. http://dx.doi.org/10.5194/nhess-22-2725-2022.

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Abstract. Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories. The onset of agriculture drought is slow and can occur over vast areas with varying spatial effects, differing in areas with a particular vegetation land cover or specific agro-ecological sub-regions. These spatial variations imply that monitoring and forecasting agricultural drought require complex models that consider the spatial variations in a given region of interest. Hierarchical Bayesian models are suited for modelling such complex systems. Using partially pooled data with sub-groups that characterise spatial differences, these models can capture the sub-group variation while allowing flexibility and information sharing between these sub-groups. This paper's objective is to improve the accuracy and precision of agricultural drought forecasting in spatially diverse regions with a hierarchical Bayesian model. Results showed that the hierarchical Bayesian model was better at capturing the variability for the different agro-ecological zones and vegetation land covers compared to a regular Bayesian auto-regression distributed lags model. The forecasted vegetation condition and associated drought probabilities were more accurate and precise with the hierarchical Bayesian model at 4- to 10-week lead times. Forecasts from the hierarchical model exhibited higher hit rates with a low probability of false alarms for drought events in semi-arid and arid zones. The hierarchical Bayesian model also showed good transferable forecast skills over counties not included in the training data.

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Wang, Mengxi, Qingwang Liu, Liyong Fu, Guangxing Wang, and Xiongqing Zhang. "Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach." Remote Sensing 11, no. 9 (May 3, 2019): 1050. http://dx.doi.org/10.3390/rs11091050.

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Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.

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Xiao, Jun, Rui Zhao, and Kin-Man Lam. "Bayesian sparse hierarchical model for image denoising." Signal Processing: Image Communication 96 (August 2021): 116299. http://dx.doi.org/10.1016/j.image.2021.116299.

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Han, Peng, Ming Mei Chen, and Ying Nan Zhang. "A Hierarchical Bayesian Model for Text Corpora." Applied Mechanics and Materials 687-691 (November 2014): 1237–40. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1237.

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We propose a new generative probabilistic Dirich- let Author-Topic (DAT) Model for extracting information about authors and topics from large text collections. DAT is a three-level hierarchical Bayesian model. The model builds on the Author Topic (AT) model, adding the key attribute that distribution over author is conditioned on a Dirichlet prior. The probability distribution over topics in a multi-author document is a mixture of the distributions associated with the authors. The three level distributions including document-author, author-topic and topic-word are learned from data in an unsupervised manner using a Gibbs sampling algorithm. We give results on a large corpus which contains 1740 papers from the Neural Information Processing Systems Conference (NIPS). Experiments based on perplexity scores for test documents are used to illustrate systematic differences between the proposed model and a number of alternatives.

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Sivaganesan, M., N. J. Adcock, and E. W. Rice. "Inactivation ofBacillusglobigiiby chlorination: A hierarchical Bayesian model." Journal of Water Supply: Research and Technology - Aqua 55, no. 1 (February 2006): 33–43. http://dx.doi.org/10.2166/aqua.2005.068.

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Tan, Sho-Ken, Yasushi Takano, and Hirohisa Kishino. "Diversified Preference and a Bayesian Hierarchical Model." Japanese Journal of Biometrics 21, no. 2 (2000): 15–28. http://dx.doi.org/10.5691/jjb.21.15.

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Chaari, Lotfi, Jean-Christophe Pesquet, Jean-Yves Tourneret, Philippe Ciuciu, and Amel Benazza-Benyahia. "A Hierarchical Bayesian Model for Frame Representation." IEEE Transactions on Signal Processing 58, no. 11 (November 2010): 5560–71. http://dx.doi.org/10.1109/tsp.2010.2055562.

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Behmanesh, Iman, Babak Moaveni, Geert Lombaert, and Costas Papadimitriou. "Hierarchical Bayesian model updating for structural identification." Mechanical Systems and Signal Processing 64-65 (December 2015): 360–76. http://dx.doi.org/10.1016/j.ymssp.2015.03.026.

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Deshpande, Sameer K., and Abraham Wyner. "A hierarchical Bayesian model of pitch framing." Journal of Quantitative Analysis in Sports 13, no. 3 (September 26, 2017): 95–112. http://dx.doi.org/10.1515/jqas-2017-0027.

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Abstract Since the advent of high-resolution pitch tracking data (PITCHf/x), many in the sabermetrics community have attempted to quantify a Major League Baseball catcher’s ability to “frame” a pitch (i.e. increase the chance that a pitch is a called as a strike). Especially in the last 3 years, there has been an explosion of interest in the “art of pitch framing” in the popular press as well as signs that teams are considering framing when making roster decisions. We introduce a Bayesian hierarchical model to estimate each umpire’s probability of calling a strike, adjusting for the pitch participants, pitch location, and contextual information like the count. Using our model, we can estimate each catcher’s effect on an umpire’s chance of calling a strike. We are then able translate these estimated effects into average runs saved across a season. We also introduce a new metric, analogous to Jensen, Shirley, and Wyner’s Spatially Aggregate Fielding Evaluation metric, which provides a more honest assessment of the impact of framing.

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Bunnin, F. O., and J. Q. Smith. "A Bayesian Hierarchical Model for Criminal Investigations." Bayesian Analysis 16, no. 1 (2021): 1–30. http://dx.doi.org/10.1214/19-ba1192.

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Mashford, John, Yong Song, Q. J. Wang, and David Robertson. "A Bayesian hierarchical spatio-temporal rainfall model." Journal of Applied Statistics 46, no. 2 (May 15, 2018): 217–29. http://dx.doi.org/10.1080/02664763.2018.1473347.

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Lockwood, John R., Kathryn Roeder, and B. Devlin. "A Bayesian hierarchical model for allele frequencies." Genetic Epidemiology 20, no. 1 (2000): 17–33. http://dx.doi.org/10.1002/1098-2272(200101)20:1<17::aid-gepi3>3.0.co;2-q.

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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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Song, Chengyuan, Dongchu Sun, Kun Fan, and Rongji Mu. "Posterior Propriety of an Objective Prior in a 4-Level Normal Hierarchical Model." Mathematical Problems in Engineering 2020 (February 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/8236934.

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The use of hierarchical Bayesian models in statistical practice is extensive, yet it is dangerous to implement the Gibbs sampler without checking that the posterior is proper. Formal approaches to objective Bayesian analysis, such as the Jeffreys-rule approach or reference prior approach, are only implementable in simple hierarchical settings. In this paper, we consider a 4-level multivariate normal hierarchical model. We demonstrate the posterior using our recommended prior which is proper in the 4-level normal hierarchical models. A primary advantage of the recommended prior over other proposed objective priors is that it can be used at any level of a hierarchical model.

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Odani, Motoi, Satoru Fukimbara, and Tosiya Sato. "A Bayesian meta-analytic approach for safety signal detection in randomized clinical trials." Clinical Trials 14, no. 2 (January 6, 2017): 192–200. http://dx.doi.org/10.1177/1740774516683920.

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Background/Aim: Meta-analyses are frequently performed on adverse event data and are primarily used for improving statistical power to detect safety signals. However, in the evaluation of drug safety for New Drug Applications, simple pooling of adverse event data from multiple clinical trials is still commonly used. We sought to propose a new Bayesian hierarchical meta-analytic approach based on consideration of a hierarchical structure of reported individual adverse event data from multiple randomized clinical trials. Methods: To develop our meta-analysis model, we extended an existing three-stage Bayesian hierarchical model by including an additional stage of the clinical trial level in the hierarchical model; this generated a four-stage Bayesian hierarchical model. We applied the proposed Bayesian meta-analysis models to published adverse event data from three premarketing randomized clinical trials of tadalafil and to a simulation study motivated by the case example to evaluate the characteristics of three alternative models. Results: Comparison of the results from the Bayesian meta-analysis model with those from Fisher’s exact test after simple pooling showed that 6 out of 10 adverse events were the same within a top 10 ranking of individual adverse events with regard to association with treatment. However, more individual adverse events were detected in the Bayesian meta-analysis model than in Fisher’s exact test under the body system “Musculoskeletal and connective tissue disorders.” Moreover, comparison of the overall trend of estimates between the Bayesian model and the standard approach (odds ratios after simple pooling methods) revealed that the posterior median odds ratios for the Bayesian model for most adverse events shrank toward values for no association. Based on the simulation results, the Bayesian meta-analysis model could balance the false detection rate and power to a better extent than Fisher’s exact test. For example, when the threshold value of the posterior probability for signal detection was set to 0.8, the false detection rate was 41% and power was 88% in the Bayesian meta-analysis model, whereas the false detection rate was 56% and power was 86% in Fisher’s exact test. Limitations: Adverse events under the same body system were not necessarily positively related when we used “system organ class” and “preferred term” in the Medical Dictionary for Regulatory Activities as a hierarchical structure of adverse events. For the Bayesian meta-analysis models to be effective, the validity of the hierarchical structure of adverse events and the grouping of adverse events are critical. Conclusion: Our proposed meta-analysis models considered trial effects to avoid confounding by trial and borrowed strength from both within and across body systems to obtain reasonable and stable estimates of an effect measure by considering a hierarchical structure of adverse events.

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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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Junaidi, Junaidi, Darfiana Nur, Irene Hudson, and Elizabeth Stojanovski. "Estimation Parameters of Dependence Meta-Analytic Model: New Techniques for the Hierarchical Bayesian Model." Computation 10, no. 5 (May 4, 2022): 71. http://dx.doi.org/10.3390/computation10050071.

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Dependence in meta-analytic models can happen due to the same collected data or from the same researchers. The hierarchical Bayesian linear model in a meta-analysis that allows dependence in effect sizes is investigated in this paper. The interested parameters on the hierarchical Bayesian linear dependence (HBLD) model which was developed using the Bayesian techniques will then be estimated. The joint posterior distribution of all parameters for the hierarchical Bayesian linear dependence (HBLD) model is obtained by applying the Gibbs sampling algorithm. Furthermore, in order to measure the robustness of the HBLD model, the sensitivity analysis is conducted using a different prior distribution on the model. This is carried out by applying the Metropolis within the Gibbs algorithm. The simulation study is performed for the estimation of all parameters in the model. The results show that the obtained estimated parameters are close to the true parameters, indicating the consistency of the parameters for the model. The model is also not sensitive because of the changing prior distribution which shows the robustness of the model. A case study, to assess the effects of native-language vocabulary aids on second language reading, is conducted successfully in testing the parameters of the models.

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Li, Qie, and Junfeng Shang. "A Bayesian Hierarchical Model for Multiple Comparisons in Mixed Models." Communications in Statistics - Theory and Methods 44, no. 23 (January 7, 2015): 5071–90. http://dx.doi.org/10.1080/03610926.2013.813042.

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Albert, Jim, and Siddhartha Chib. "Bayesian Tests and Model Diagnostics in Conditionally Independent Hierarchical Models." Journal of the American Statistical Association 92, no. 439 (September 1997): 916–25. http://dx.doi.org/10.1080/01621459.1997.10474046.

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Baranowski, Jerzy. "Predicting IoT failures with Bayesian workflow." Eksploatacja i Niezawodnosc - Maintenance and Reliability 24, no. 2 (March 12, 2022): 248–59. http://dx.doi.org/10.17531/ein.2022.2.6.

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IoT networks are so voluminous that they cannot be treated as individual devices, but as populations. Main aim of the paper is to create a comprehensive method for predicting failures taking device variance into consideration. We propose using data fusion of happenstance observations (resets and failures) to better estimate device parameters. We propose using methods of population analysis in Bayesian statistics to estimate failure times investigating only a part of the population. For this purpose, we use multilevel hierarchical Bayesian model and provide it with post stratification. We propose model assumptions, construct the model and evaluate it, and perform computations using Hamiltonian Monte Carlo. This method is known as the Bayesian workflow. We have analyzed three different models showing that, in case of small device variance, it can be ignored, or at least compensated, while significant differences require hierarchical modeling. We also show that hierarchical model shows significant robustness to a small amount of data. We have shown attractiveness of Bayesian approach to modeling failures of IoT devices. Ability to diagnose and tune models, and assure their computational fidelity is a great advantage of Bayesian workflow.

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Noncheva, Veska, Maria Dobreva, and Ivan Chenchev. "Bayesian Hierarchical Model of Width of Keratinized Gingiva." IOSR Journal of Mathematics 13, no. 01 (February 2017): 14–18. http://dx.doi.org/10.9790/5728-1301041418.

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Jo, Aejung, Hyungon Cho, and Gwangseob Kim. "Nonstationary Frequency Analysis Using a Hierarchical Bayesian Model." Journal of Korean Society of Hazard Mitigation 15, no. 5 (October 31, 2015): 19–24. http://dx.doi.org/10.9798/kosham.2015.15.5.19.

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Bellot, Alexis, and Mihaela van der Schaar. "A Hierarchical Bayesian Model for Personalized Survival Predictions." IEEE Journal of Biomedical and Health Informatics 23, no. 1 (January 2019): 72–80. http://dx.doi.org/10.1109/jbhi.2018.2832599.

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Shigemasu, Kazuo, and Shin-ichi Mayekawa. "A BAYESIAN HIERARCHICAL LINEAR MODEL WITH EDUCATIONAL APPLICATIONS." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 26, no. 1 (1996): 1–23. http://dx.doi.org/10.14490/jjss1995.26.1.

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Hurn, Merrilee, Peter J. Green, and Fahimah Al-Awadhi. "A Bayesian hierarchical model for photometric red shifts." Journal of the Royal Statistical Society: Series C (Applied Statistics) 57, no. 4 (September 2008): 487–504. http://dx.doi.org/10.1111/j.1467-9876.2008.00621.x.

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Hinton, S. R., T. M. Davis, A. G. Kim, D. Brout, C. B. D’Andrea, R. Kessler, J. Lasker, et al. "Steve: A Hierarchical Bayesian Model for Supernova Cosmology." Astrophysical Journal 876, no. 1 (April 29, 2019): 15. http://dx.doi.org/10.3847/1538-4357/ab13a3.

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Ho, Yen-Yi, Tien Nhu Vo, Haitao Chu, Xianghua Luo, and Chap T. Le. "A Bayesian hierarchical model for demand curve analysis." Statistical Methods in Medical Research 27, no. 7 (October 20, 2016): 2038–49. http://dx.doi.org/10.1177/0962280216673675.

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Drug self-administration experiments are a frequently used approach to assessing the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration’s policy on tobacco regulation, because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

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Micheas, Athanasios C., and Christopher K. Wikle. "A Bayesian Hierarchical Nonoverlapping Random Disc Growth Model." Journal of the American Statistical Association 104, no. 485 (March 2009): 274–83. http://dx.doi.org/10.1198/jasa.2009.0124.

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Zhang, Min, Robert L. Strawderman, Mark E. Cowen, and Martin T. Wells. "Bayesian Inference for a Two-Part Hierarchical Model." Journal of the American Statistical Association 101, no. 475 (September 2006): 934–45. http://dx.doi.org/10.1198/016214505000001429.

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Delgado, H. E., L. M. Sarro, G. Clementini, T. Muraveva, and A. Garofalo. "Hierarchical Bayesian model to inferPL(Z)relations usingGaiaparallaxes." Astronomy & Astrophysics 623 (March 2019): A156. http://dx.doi.org/10.1051/0004-6361/201832945.

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In a recent study we analysed period–luminosity–metallicity (PLZ) relations for RR Lyrae stars using theGaiaData Release 2 (DR2) parallaxes. It built on a previous work that was based on the firstGaiaData Release (DR1), and also included period–luminosity (PL) relations for Cepheids and RR Lyrae stars. The method used to infer the relations fromGaiaDR2 data and one of the methods used forGaiaDR1 data was based on a Bayesian model, the full description of which was deferred to a subsequent publication. This paper presents the Bayesian method for the inference of the parameters ofPL(Z) relations used in those studies, the main feature of which is to manage the uncertainties on observables in a rigorous and well-founded way. The method encodes the probability relationships between the variables of the problem in a hierarchical Bayesian model and infers the posterior probability distributions of thePL(Z) relationship coefficients using Markov chain Monte Carlo simulation techniques. We evaluate the method with several semi-synthetic data sets and apply it to a sample of 200 fundamental and first-overtone RR Lyrae stars for whichGaiaDR1 parallaxes and literatureKs-band mean magnitudes are available. We define and test several hyperprior probabilities to verify their adequacy and check the sensitivity of the solution with respect to the prior choice. The main conclusion of this work, based on the test with semi-syntheticGaiaDR1 parallaxes, is the absolute necessity of incorporating the existing correlations between the period, metallicity, and parallax measurements in the form of model priors in order to avoid systematically biased results, especially in the case of non-negligible uncertainties in the parallaxes. The relation coefficients obtained here have been superseded by those presented in our recent paper that incorporates the findings of this work and the more recentGaiaDR2 measurements.

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Yuan, Tao, and Yizhen Ji. "A Hierarchical Bayesian Degradation Model for Heterogeneous Data." IEEE Transactions on Reliability 64, no. 1 (March 2015): 63–70. http://dx.doi.org/10.1109/tr.2014.2354934.

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El Korso, Mohammed Nabil, Remy Boyer, Pascal Larzabal, and Bernard-Henri Fleury. "Estimation Performance for the Bayesian Hierarchical Linear Model." IEEE Signal Processing Letters 23, no. 4 (April 2016): 488–92. http://dx.doi.org/10.1109/lsp.2016.2528579.

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Mouria-beji, Fériel. "A hierarchical Bayesian model for continuous speech recognition." Pattern Recognition Letters 23, no. 7 (May 2002): 773–81. http://dx.doi.org/10.1016/s0167-8655(01)00154-4.

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Jun Li and Dacheng Tao. "A Bayesian Hierarchical Factorization Model for Vector Fields." IEEE Transactions on Image Processing 22, no. 11 (November 2013): 4510–21. http://dx.doi.org/10.1109/tip.2013.2274732.

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Kennedy, Marc C., Victoria J. Roelofs, Clive W. Anderson, and José Domingo Salazar. "A hierarchical Bayesian model for extreme pesticide residues." Food and Chemical Toxicology 49, no. 1 (January 2011): 222–32. http://dx.doi.org/10.1016/j.fct.2010.10.020.

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Chen, Geng, and Sheng Luo. "Robust Bayesian hierarchical model using normal/independent distributions." Biometrical Journal 58, no. 4 (December 29, 2015): 831–51. http://dx.doi.org/10.1002/bimj.201400255.

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Cooley, Daniel, Philippe Naveau, Vincent Jomelli, Antoine Rabatel, and Delphine Grancher. "A Bayesian hierarchical extreme value model for lichenometry." Environmetrics 17, no. 6 (2006): 555–74. http://dx.doi.org/10.1002/env.764.

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Su, Zhenming, Milo D. Adkison, and Benjamin W. Van Alen. "A hierarchical Bayesian model for estimating historical salmon escapement and escapement timing." Canadian Journal of Fisheries and Aquatic Sciences 58, no. 8 (August 1, 2001): 1648–62. http://dx.doi.org/10.1139/f01-099.

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In this paper, we present an improved methodology for estimating salmon escapements from stream count data. The new method uses a hierarchical Bayesian model that improves estimates in years when data are sparse by "borrowing strength" from counts in other years. We present a model of escapement and of count data, a hierarchical Bayesian statistical framework, a Gibbs sampling approach for evaluation of the posterior distributions of the quantities of interest, and criteria for determining when the model and inference are adequate. We then apply the hierarchical Bayesian model to estimating historical escapement and escapement timing for pink salmon (Oncorhynchus gorbuscha) returns to Kadashan Creek in Southeast Alaska.

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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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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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Kim, Jin-Young, Hyun-Han Kwon, and Jeong-Yeul Lim. "Development of Hierarchical Bayesian Spatial Regional Frequency Analysis Model Considering Geographical Characteristics." Journal of Korea Water Resources Association 47, no. 5 (May 31, 2014): 469–82. http://dx.doi.org/10.3741/jkwra.2014.47.5.469.

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Guo, Xiaoyu, Lingtao Wu, Yajie Zou, and Lee Fawcett. "Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (June 10, 2019): 111–21. http://dx.doi.org/10.1177/0361198119849899.

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Hotspot identification is an important step in the highway safety management process. Errors in hotspot identification (HSID) may result in an inefficient use of limited resources for safety improvements. The empirical Bayesian (EB) HSID has been widely applied as an effective approach in identifying hotspots. However, there are some limitations with the EB approach. It assumes that the parameter estimates of the safety performance function (SPF) are correct without any uncertainty, and does not consider temporal instability in crashes, which has been reported in recent studies. The Bayesian hierarchical model is an emerging technique that addresses the limitations of the EB method. Thus, the objective of this study is to compare the performance of the standard EB method and the Bayesian hierarchical model in identifying hotspots. Three methods (crash rate, EB, and the Bayesian hierarchical model) were applied to identify risky intersections with different significance levels. Four evaluation tests (site consistency, method consistency, total rank differences, and Poisson mean differences tests) were conducted to assess the performance of these three methods. The testing results suggest that: (1) the Bayesian hierarchical model outperforms the crash rate and the EB methods in most cases, and the Bayesian hierarchical model improves the accuracy of HSID significantly; and (2) hotspots identified with crash rates are generally unreliable. This is significant for roadway agencies and practitioners trying to accurately rank sites in the roadway network to effectively manage safety investments. Roadway agencies and practitioners are encouraged to consider the Bayesian hierarchical model in identifying hotspots.

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AGBAJE, Olorunsola F., Stephen D. LUZIO, Ahmed I. S. ALBARRAK, David J. LUNN, David R. OWENS, and Roman HOVORKA. "Bayesian hierarchical approach to estimate insulin sensitivity by minimal model." Clinical Science 105, no. 5 (November 1, 2003): 551–60. http://dx.doi.org/10.1042/cs20030117.

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We adopted Bayesian analysis in combination with hierarchical (population) modelling to estimate simultaneously population and individual insulin sensitivity (SI) and glucose effectiveness (SG) with the minimal model of glucose kinetics using data collected during insulin-modified intravenous glucose tolerance test (IVGTT) and made comparison with the standard non-linear regression analysis. After fasting overnight, subjects with newly presenting Type II diabetes according to World Health Organization criteria (n=65; 53 males, 12 females; age, 54±9 years; body mass index, 30.4±5.2 kg/m2; means±S.D.) underwent IVGTT consisting of a 0.3 g of glucose bolus/kg of body weight given at time zero for 2 min, followed by 0.05 unit of insulin/kg of body weight at 20 min. Bayesian inference was carried out using vague prior distributions and log-normal distributions to guarantee non-negativity and, thus, physiological plausibility of model parameters and associated credible intervals. Bayesian analysis gave estimates of SI in all subjects. Non-linear regression analysis failed in four cases, where Bayesian analysis-derived SI was located in the lower quartile and was estimated with lower precision. The population means of SI and SG provided by Bayesian analysis and non-linear regression were identical, but the interquartile range given by Bayesian analysis was tighter by approx. 20% for SI and by approx. 15% for SG. Individual insulin sensitivities estimated by the two methods were highly correlated (rS=0.98; P<0.001). However, the correlation in the lower 20% centile of the insulin-sensitivity range was significantly lower than the correlation in the upper 80% centile (rS=0.71 compared with rS=0.99; P<0.001). We conclude that the Bayesian hierarchical analysis is an appealing method to estimate SI and SG, as it avoids parameter estimation failures, and should be considered when investigating insulin-resistant subjects.

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Journal articles on the topic 'Bayesian hierarchical model'

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