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Relevant bibliographies by topics / Hierarchal Bayesian statistics

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Academic literature on the topic 'Hierarchal Bayesian statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Hierarchal Bayesian statistics"

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Yada, Shinjo, and Chikuma Hamada. "A Bayesian hierarchal modeling approach to shortening phase I/II trials of anticancer drug combinations." Pharmaceutical Statistics 17, no. 6 (August 15, 2018): 750–60. http://dx.doi.org/10.1002/pst.1895.

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Zhuang, Lili, and Noel Cressie. "Bayesian hierarchical statistical SIRS models." Statistical Methods & Applications 23, no. 4 (November 2014): 601–46. http://dx.doi.org/10.1007/s10260-014-0280-9.

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Price, M. A., J. D. McEwen, X. Cai, and T. D. Kitching (for the LSST Dark Energy Science Collaboration). "Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations." Monthly Notices of the Royal Astronomical Society 489, no. 3 (August 26, 2019): 3236–50. http://dx.doi.org/10.1093/mnras/stz2373.

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ABSTRACT Weak lensing convergence maps – upon which higher order statistics can be calculated – can be recovered from observations of the shear field by solving the lensing inverse problem. For typical surveys this inverse problem is ill-posed (often seriously) leading to substantial uncertainty on the recovered convergence maps. In this paper we propose novel methods for quantifying the Bayesian uncertainty in the location of recovered features and the uncertainty in the cumulative peak statistic – the peak count as a function of signal-to-noise ratio (SNR). We adopt the sparse hierarchical Bayesian mass-mapping framework developed in previous work, which provides robust reconstructions and principled statistical interpretation of reconstructed convergence maps without the need to assume or impose Gaussianity. We demonstrate our uncertainty quantification techniques on both Bolshoi N-body (cluster scale) and Buzzard V-1.6 (large-scale structure) N-body simulations. For the first time, this methodology allows one to recover approximate Bayesian upper and lower limits on the cumulative peak statistic at well-defined confidence levels.

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Ghosh, Malay, Tapabrata Maiti, Dalho Kim, Sounak Chakraborty, and Ashutosh Tewari. "Hierarchical Bayesian Neural Networks." Journal of the American Statistical Association 99, no. 467 (September 2004): 601–8. http://dx.doi.org/10.1198/016214504000000665.

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Telesca, Donatello, and Lurdes Y. T. Inoue. "Bayesian Hierarchical Curve Registration." Journal of the American Statistical Association 103, no. 481 (March 1, 2008): 328–39. http://dx.doi.org/10.1198/016214507000001139.

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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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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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Han, Ming. "E-Bayesian Estimation and Hierarchical Bayesian Estimation of Failure Probability." Communications in Statistics - Theory and Methods 40, no. 18 (September 15, 2011): 3303–14. http://dx.doi.org/10.1080/03610926.2010.498643.

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AlKheder, Sharaf, and Moudhi Al-Rashidi. "Bayesian hierarchical statistics for traffic safety modelling and forecasting." International Journal of Injury Control and Safety Promotion 27, no. 2 (September 18, 2019): 99–111. http://dx.doi.org/10.1080/17457300.2019.1665550.

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Angers, Jean-François, and Mohan Delampady. "Hierarchical bayesian curve fitting and smoothing." Canadian Journal of Statistics 20, no. 1 (March 1992): 35–49. http://dx.doi.org/10.2307/3315573.

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Dissertations / Theses on the topic "Hierarchal Bayesian statistics"

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Cheng, Si. "Hierarchical Nearest Neighbor Co-kriging Gaussian Process For Large And Multi-Fidelity Spatial Dataset." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613750570927821.

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Lawson, Elizabeth Anne. "Autologous Stem Cell Transplant: Factors Predicting the Yield of CD34+ Cells." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1144.pdf.

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Seat, Marlee Lyn. "Using LiDAR Data to Analyze Access Management Criteria in Utah." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6329.

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The Utah Department of Transportation (UDOT) has completed a Light Detection and Ranging (LiDAR) data inventory that includes access locations across the UDOT network. The new data are anticipated to be extremely useful in better defining safety and in completing a systemwide analysis of locations where safety could be improved, or where safety has been improved across the state. The Department of Civil and Environmental Engineering at Brigham Young University (BYU) has worked with the new data to perform a safety analysis of the state related to access management, particularly related to driveway spacing and raised medians. The primary objective of this research was to increase understanding of the safety impacts across the state related to access management. These objectives were accomplished by using the LiDAR database to evaluate driveway spacing and locations to aid in hot spot identification and to develop relationships between access design and location as a function of safety and access category (AC). Utah Administrative Rule R930-6 contains access management guidelines to balance the access found on a roadway with traffic and safety operations. These guidelines were used to find the maximum number of driveways recommended for a roadway. ArcMap 10.3 and Microsoft Excel were used to visualize the data and identify hot spot locations. An analysis conducted in this study compared current roadway characteristics to the R930-6 guidelines to find locations where differences occurred. This analysis does not indicate the current AC is incorrect; it simply means that the assigned AC does not meet current roadway characteristic based on the LiDAR data analysis. UDOT can decide what this roadway will become in the future and help shape each segment using the AC outlined in the R930-6. A hierarchal Bayesian statistical before-after model, created in previous BYU safety research, was used to analyze locations where raised medians have been installed. Twenty locations where raised medians were installed in Utah between 2002 to 2014 were used in this model. The model analyzed the raised medians by AC. Only three AC were represented in the data. Regression plots depicting a decrease in crashes before and after installation, posterior distribution plots showing the probability of a decrease in crashes after installation, and crash modification factor (CMF) plots presenting the CMF values estimated for different vehicle miles traveled (VMT) values were all created as output from the before-after model. Overall, installing a raised median gives an approximate reduction of 53 percent for all crashes. Individual AC analysis yielded results ranging from 32 to 44 percent for all severity groups except severity 4 and 5. When the model was only run for crash severity 4 and 5, a larger reduction of 57 to 58 percent was found.

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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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Brody-Moore, Peter. "Bayesian Hierarchical Meta-Analysis of Asymptomatic Ebola Seroprevalence." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2228.

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The continued study of asymptomatic Ebolavirus infection is necessary to develop a more complete understanding of Ebola transmission dynamics. This paper conducts a meta-analysis of eight studies that measure seroprevalence (the number of subjects that test positive for anti-Ebolavirus antibodies in their blood) in subjects with household exposure or known case-contact with Ebola, but that have shown no symptoms. In our two random effects Bayesian hierarchical models, we find estimated seroprevalences of 8.76% and 9.72%, significantly higher than the 3.3% found by a previous meta-analysis of these eight studies. We also produce a variation of this meta-analysis where we exclude two of the eight studies. In this model, we find an estimated seroprevalence of 4.4%, much lower than our first two Bayesian hierarchical models. We believe a random effects model more accurately reflects the heterogeneity between studies and thus asymptomatic Ebola is more seroprevalent than previously believed among subjects with household exposure or known case-contact. However, a strong conclusion cannot be reached on the seriousness of asymptomatic Ebola without an international testing standard and more data collection using this adopted standard.

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Southey, Richard. "Bayesian hierarchical modelling with application in spatial epidemiology." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/59489.

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Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.

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Bao, Haikun. "Bayesian hierarchical regression model to detect quantitative trait loci /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/baoh/haikunbao.pdf.

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McBride, John Jacob Bratcher Thomas L. "Conjugate hierarchical models for spatial data an application on an optimal selection procedure /." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/3955.

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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.

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George, Robert Emerson. "The role of hierarchical priors in robust Bayesian inference /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487847761308082.

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Books on the topic "Hierarchal Bayesian statistics"

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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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Lawson, Andrew. Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. Boca Raton: Taylor & Francis, 2008.

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Kéry, Marc. Bayesian population analysis using WinBUGS: A hierarchical perspective. Boston: Academic Press, 2011.

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

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(Editor), James S. Clark, and Alan Gelfand (Editor), eds. Hierarchical Modelling for the Environmental Sciences. Oxford University Press, USA, 2006.

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

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1957-, Clark James Samuel, and Gelfand Alan E. 1945-, eds. Hierarchical modelling for the environmental sciences: Statistical methods and applications. Oxford: Oxford University Press, 2006.

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Computational statistics: Hierarchical Bayes and MCMC methods in the environmental sciences. New York: Oxford University Press, 2006.

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(Editor), James S. Clark, and Alan Gelfand (Editor), eds. Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications (Oxford Biology). Oxford University Press, USA, 2006.

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Book chapters on the topic "Hierarchal Bayesian statistics"

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Klugman, Stuart A. "The Hierarchical Bayesian Approach." In Bayesian Statistics in Actuarial Science, 65–79. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-017-0845-6_6.

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Mardia, Kanti V., and Vysaul B. Nyirongo. "Bayesian Hierarchical Alignment Methods." In Statistics for Biology and Health, 209–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27225-7_9.

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Klugman, Stuart A. "The Hierarchical Normal Linear Model." In Bayesian Statistics in Actuarial Science, 81–113. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-017-0845-6_7.

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Erkanli, Alaattin, Refik Soyer, and Dalene Stangl. "Hierarchical Bayesian Analysis for Prevalence Estimation." In Case Studies in Bayesian Statistics, 325–46. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2290-3_8.

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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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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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Khatri, C. G., and C. Radhakrishna Rao. "Empirical Hierarchical Bayes Estimation." In Bayesian Analysis in Statistics and Econometrics, 147–61. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2944-5_8.

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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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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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Klugman, Stuart A. "Modifications to the Hierarchical Normal Linear Model." In Bayesian Statistics in Actuarial Science, 151–57. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-017-0845-6_9.

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Conference papers on the topic "Hierarchal Bayesian statistics"

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Bhering, F. L., A. Polpo, and C. A. De B. Pereira. "A hierarchical Weibull Bayesian model for series and parallel systems." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759589.

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Mossavat, Iman, and Oliver Amft. "Sparse Bayesian hierarchical mixture of experts." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967785.

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Vadde, S., R. S. Krishnamachari, F. Mistree, and J. K. Allen. "The Bayesian Compromise Decision Support Problem for Hierarchical Design Involving Uncertainty." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0088.

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Abstract In this paper we present an extension to the traditional compromise Decision Support Problem (DSP) formulation. In this formulation we use Bayesian Statistics to model uncertainties associated with the information being used. In an earlier paper we have introduced a compromise DSP that accounts for uncertainty using fuzzy set theory. In this paper we describe the Bayesian Decision Support Problem. We use this formulation to design a portal frame structure. We discuss the results and compare them with those obtained using the Fuzzy DSP. Finally, we discuss the efficacy of incorporating Bayesian Statistics into the traditional compromise DSP formulation and describe some of the pending research issues.

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Mott, John H. "Hierarchical Bayesian MCMC Estimation of Airport Operations Counts." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450701.

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Szacherski, Pascal, Jean-Francois Giovannelli, and Pierre Grangeat. "Joint Bayesian hierarchical inversion-classification and application in proteomics." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967636.

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Zhu, Dongxiao, and Alfred Hero. "Bayesian hierarchical model for estimating gene association network from microarray data." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353141.

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Artiono, Rudianto. "Bayesian hierarchical model for mapping positive patient Covid-19 in Surabaya, Indonesia." In INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042113.

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Jennings, Elizabeth M., Jeffrey S. Morris, Raymond J. Carroll, Ganiraju C. Manyam, and Veerabhadran Baladandayuthapani. "Hierarchical Bayesian methods for integration of various types of genomics data." In 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2012. http://dx.doi.org/10.1109/gensips.2012.6507713.

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Srivastava, Sanvesh, Wenyi Wang, Pascal O. Zinn, Rivka R. Colen, and Veerabhadran Baladandayuthapani. "Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines." In 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2012. http://dx.doi.org/10.1109/gensips.2012.6507716.

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Hu, Min, Yongjing Liu, and Haiping Ren. "Hierarchical Bayesian and E-Bayesian Statistical Inference of Reliability of Geometric Distribution." In 2019 International Conference on Information Technology and Computer Application (ITCA). IEEE, 2019. http://dx.doi.org/10.1109/itca49981.2019.00048.

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