Parallel inference for big data with the group Bayesian method
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DOI: 10.1007/s00184-020-00784-0
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- Matias Quiroz & Robert Kohn & Mattias Villani & Minh-Ngoc Tran, 2019.
"Speeding Up MCMC by Efficient Data Subsampling,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 831-843, April.
- Quiroz, Matias & Villani, Mattias & Kohn, Robert, 2015. "Speeding Up Mcmc By Efficient Data Subsampling," Working Paper Series 297, Sveriges Riksbank (Central Bank of Sweden).
- Kohn, Robert & Quiroz, Matias & Tran, Minh-Ngoc & Villani, Mattias, 2016. "Speeding up MCMC by Efficient Data Subsampling," Working Papers 2123/16205, University of Sydney Business School, Discipline of Business Analytics.
- Faming Liang & Qifan Song & Kai Yu, 2013. "Bayesian Subset Modeling for High-Dimensional Generalized Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 589-606, June.
- repec:dau:papers:123456789/5671 is not listed on IDEAS
- Michael I. Jordan & Jason D. Lee & Yun Yang, 2019. "Communication-Efficient Distributed Statistical Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 668-681, April.
- Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
- Denwood, Matthew J., 2016. "runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i09).
- Ping Zeng & Xiang Zhou, 2017. "Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
- Qifan Song & Faming Liang, 2015. "A split-and-merge Bayesian variable selection approach for ultrahigh dimensional regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(5), pages 947-972, November.
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Keywords
Data subsets; Group Gibbs; Parallel inference;All these keywords.
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