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B
Banerjee, A., J. Murray, and D. B. Dunson. "Bayesian learning of joint distributions of objects." In Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 2013., 2013, available at http://jmlr.csail.mit.edu/proceedings/papers/v31/banerjee13a.html.
Bradley, Jonathan R., Christopher K. Wikle, and Scott H. Holan. "Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey." Journal of the American Statistical Association (2015). DOI: 10.1080/01621459.2015.1117471, available at http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1117471.
Bradley, Jonathan, C.K. Wikle, and S. H. Holan. "Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey." Journal of the American Statistical Association (2015). DOI: 10.1080/01621459.2015.1117471, available at http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1117471.
Bradley, J.R., C.K. Wikle, and S.H. Holan. "Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey." Journal of the American Statistical Association (2016): 472-487, available at https://arxiv.org/abs/1405.7227.
Bradley, J.R., S.H. Holan, and C.K. Wikle. "Bayesian Hierarchical Models with Conjugate Full-Conditional Distributions for Dependent Data from the Natural Exponential Family." Journal of the American Statistical Association - T&M. (2016), available at https://arxiv.org/abs/1701.07506.
Bradley, J. R., C.K. Wikle, and S. H. Holan. "Bayesian Spatial Change of Support for Count–Valued Survey Data." ArXiv, no. 1405.7227 (2015), available at http://arxiv.org/abs/1405.7227.
C
Cressie, N.. Bayesian inference for the Spatial Random Effects Model, Department of Statistics, Macquarie University. Macquarie University, 2013.
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De Yoreo, M., and A. Kottas. "A Bayesian nonparametric Markovian model for nonstationary time series." Statistics and Computing (2016).
DeYoreo, Maria, and Athanasios Kottas. A Bayesian nonparametric Markovian model for nonstationary time series. ArXiv 1601.04331, 2016, available at http://arxiv.org/abs/1601.04331.
DeYoreo, M., and A. Kottas. Bayesian Nonparametric Modeling for Multivariate Ordinal Regression. ArXiv 1408.1027, 2014, available at http://arxiv.org/abs/1408.1027.
DeYoreo, Maria, and Jerome P. Reiter. Bayesian mixture modeling for multivariate conditional distributions. ArXiv 1606.04457, 2016, available at http://arxiv.org/abs/1606.04457.
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Hahn, P. R., J. S. Murray, and I. Manolopoulou. "A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct." Journal of the American Statistical Association 111, no. 513 (2016): 14-26. DOI: 10.1080/01621459.2015.1084307, available at http://www.tandfonline.com/doi/full/10.1080/01621459.2015.1084307.
Hall, R., R. Steorts, and S. E. Fienberg. "Bayesian Parametric and Nonparametric Inference for Multiple Record Likage." In Modern Nonparametric Methods in Machine Learning Workshop. NIPS, 2012, available at http://www.stat.cmu.edu/NCRN/PUBLIC/files/beka_nips_finalsub4.pdf.
Holan, S.H.. Bayesian Multiscale Multiple Imputation With Implications to Data Confidentiality., 2012.
Holan, S.H.. A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys, Institute of Public Policy and the Truman School of Public Affairs., 2013.
Holan, S.H.. A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys., 2014.
Holan, S.H.. "Bayesian Dynamic Time-Frequency Estimation." In Twelfth World Meeting of ISBA. Cancun, Mexico: ISBA, 2014.
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Li, Ping, Anshumali Shrivastava, and Arnd Christian König. "b-Bit Minwise Hashing in Practice." In Internetware'13., 2013, available at http://www.nudt.edu.cn/internetware2013/.
Li, Ping, Anshumali Shrivastava, and Arnd Christian König. b-Bit Minwise Hashing in Practice. Cornell University Preprint 1813:37986, 2013, available at http://hdl.handle.net/1813/37986.
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Manrique-Vallier, D., and J.P. Reiter. "Bayesian estimation of discrete multivariate latent structure models with structural zeros." Journal of Computational and Graphical Statistics 23 (2014): 1061-1079.
Manrique-Vallier, D., and J.P. Reiter. "Bayesian multiple imputation for large-scale categorical data with structural zeros." Survey Methodology 40 (2014): 125-134, available at http://www.stat.duke.edu/ jerry/Papers/SurvMeth14.pdf.
Manrique-Vallier, Daniel, and Jerome P. Reiter. "Bayesian Simultaneous Edit and Imputation for Multivariate Categorical Data." Journal of the American Statistical Association (2016). DOI: 10.1080/01621459.2016.1231612, available at http://dx.doi.org/10.1080/01621459.2016.1231612.
Manrique-Vallier, D., and J. P. Reiter. Bayesian multiple imputation for large-scale categorical data with structural zeros. Duke University / National Institute of Statistical Sciences (NISS) Preprint 1813:34889, 2013, available at http://hdl.handle.net/1813/34889.
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Porter, A.T., S.H. Holan, and C.K. Wikle. "Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models." Journal of Statistical Planning and Inference 165 (2015): 78-90. DOI: 10.1016/j.jspi.2015.04.002.
Porter, A.T.. Big Data Methodology Applied to Small Area Estimation., 2014.

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