{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T06:48:07Z","timestamp":1775630887901,"version":"3.50.1"},"reference-count":80,"publisher":"Informa UK Limited","issue":"4","funder":[{"DOI":"10.13039\/100000001","name":"U.S. National Science Foundation","doi-asserted-by":"crossref","award":["SES-1853099"],"award-info":[{"award-number":["SES-1853099"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational and Graphical Statistics"],"published-print":{"date-parts":[[2021,10,2]]},"DOI":"10.1080\/10618600.2021.1923518","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T14:55:30Z","timestamp":1620140130000},"page":"889-905","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":7,"title":["An Approach to Incorporate Subsampling Into a Generic Bayesian Hierarchical Model"],"prefix":"10.1080","volume":"30","author":[{"given":"Jonathan R.","family":"Bradley","sequence":"first","affiliation":[{"name":"Department of Statistics, Florida State University, Tallahassee, FL"}]}],"member":"301","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxu005"},{"key":"e_1_3_2_3_1","volume-title":"Hierarchical Modeling and Analysis for Spatial Data","author":"Banerjee S.","year":"2015","unstructured":"Banerjee, S., Carlin, B. P., and Gelfand, A. E. (2015), Hierarchical Modeling and Analysis for Spatial Data, London, UK: Chapman and Hall."},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2008.00663.x"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2017.08.004"},{"key":"e_1_3_2_6_1","first-page":"1515","article-title":"\u201cOn Markov chain Monte Carlo Methods for Tall Data,\u201d","volume":"18","author":"Bardenet R.","year":"2017","unstructured":"Bardenet, R., Doucet, A., and Holmes, C. (2017), \u201cOn Markov chain Monte Carlo Methods for Tall Data,\u201d The Journal of Machine Learning Research, 18, 1515\u20131557.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_7_1","first-page":"405","volume-title":"Proceedings of the 30th International Conference on Machine Learning (ICML\u201914)","author":"Bardenet R.","year":"2014","unstructured":"Bardenet, R., Doucet, A., and Holmes, C. H. (2014), \u201cTowards Scaling Up Markov Chain Monte Carlo: An Adaptive Subsampling Approach,\u201d in Proceedings of the 30th International Conference on Machine Learning (ICML\u201914), pp. 405\u2013413."},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176350043"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(78)90017-4"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-5430-7_3"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116466"},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1974.tb00999.x"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1986.tb01412.x"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asr054"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOAS862"},{"key":"e_1_3_2_16_1","first-page":"3393","volume-title":"Proceedings of the 2011 Joint Statistical Meetings","author":"Bradley J. R.","year":"2011","unstructured":"Bradley, J. R., Cressie, N., and Shi, T. (2011), \u201cSelection of Rank and Basis Functions in the Spatial Random Effects Model,\u201d in Proceedings of the 2011 Joint Statistical Meetings, 3393\u20133406. Alexandria, VA: American Statistical Association."},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11749-014-0415-1"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2019.1677471"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.5705\/ss.202016.0230"},{"key":"e_1_3_2_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/jtsa.12468"},{"key":"e_1_3_2_21_1","first-page":"1727","volume-title":"Advances in Neural Information Processing Systems","author":"Broderick T.","year":"2013","unstructured":"Broderick, T., Boyd, N., Wibisono, A., Wilson, A. C., and Jordan, M. I. (2013), \u201cStreaming Variational Bayes.\u201d In Advances in Neural Information Processing Systems, 1727\u20131735."},{"key":"e_1_3_2_22_1","article-title":"\u201cAutomated Scalable Bayesian Inference via Hilbert Coresets,\u201d","author":"Campbell T.","year":"2017","unstructured":"Campbell, T., and Broderick, T. (2017), \u201cAutomated Scalable Bayesian Inference via Hilbert Coresets,\u201d arXiv preprint arXiv:1710.05053.","journal-title":"arXiv preprint arXiv:1710.05053"},{"key":"e_1_3_2_23_1","article-title":"\u201cScalable Metropolis\u2013Hastings for Exact Bayesian Inference With Large Datasets,\u201d","author":"Cornish R.","year":"2019","unstructured":"Cornish, R., Vanetti, P., Bouchard-C\u00f4t\u00e9, A., Deligiannidis, G., and Doucet, A. (2019), \u201cScalable Metropolis\u2013Hastings for Exact Bayesian Inference With Large Datasets,\u201d arXiv preprint: 1901.09881.","journal-title":"arXiv preprint: 1901.09881"},{"key":"e_1_3_2_24_1","first-page":"1","volume-title":"Australian Academy of Science Elizabeth and Frederick White Conference","author":"Cressie N.","year":"2006","unstructured":"Cressie, N., and Johannesson, G. (2006), \u201cSpatial Prediction for Massive Data Sets,\u201d sn Australian Academy of Science Elizabeth and Frederick White Conference, Canberra: Australian Academy of Science, pp. 1\u201311."},{"key":"e_1_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.00633.x"},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.09051"},{"key":"e_1_3_2_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/cjs.10063"},{"key":"e_1_3_2_28_1","volume-title":"Statistics for Spatio-Temporal Data","author":"Cressie N.","year":"2011","unstructured":"Cressie, N., and Wikle, C. K. (2011), Statistics for Spatio-Temporal Data, Hoboken, NJ: Wiley."},{"issue":"100","key":"e_1_3_2_29_1","first-page":"1","article-title":"\u201cHamiltonian Monte Carlo With Energy Conserving Subsampling","volume":"20","author":"Dang K.-D.","year":"2019","unstructured":"Dang, K.-D., Quiroz, M., Kohn, R., Tran, M.-N., and Villani, M. (2019), \u201cHamiltonian Monte Carlo With Energy Conserving Subsampling,\u201d Journal of Machine Learning Research, 20, 100, 1\u201331.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2008.09.008"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.1198\/106186006X132178"},{"key":"e_1_3_2_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2019.100357"},{"key":"e_1_3_2_34_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1990.10476213"},{"key":"e_1_3_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.1984.4767596"},{"key":"e_1_3_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/PL00011451"},{"key":"e_1_3_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-7152(02)00099-8"},{"key":"e_1_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.1068\/a37378"},{"key":"e_1_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2018.1437476"},{"key":"e_1_3_2_40_1","article-title":"\u201cFast Inference for Intractable Likelihood Problems Using Variational Bayes,\u201d","author":"Gunawan D.","year":"2017","unstructured":"Gunawan, D., Tran, M.-N., and Kohn, R. (2017), \u201cFast Inference for Intractable Likelihood Problems Using Variational Bayes,\u201d arXiv preprint: 1705.06679.","journal-title":"arXiv preprint: 1705.06679"},{"key":"e_1_3_2_41_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2015.1102763"},{"key":"e_1_3_2_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13253-018-00348-w"},{"key":"e_1_3_2_43_1","first-page":"4080","article-title":"\u201cCoresets for Scalable Bayesian Logistic Regression,\u201d","volume":"29","author":"Huggins J.","year":"2016","unstructured":"Huggins, J., Campbell, T., and Broderick, T. (2016), \u201cCoresets for Scalable Bayesian Logistic Regression,\u201d Advances in Neural Information Processing Systems, 29, 4080\u20134088.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_44_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs12182950"},{"key":"e_1_3_2_45_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346300"},{"key":"e_1_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.tm09680"},{"key":"e_1_3_2_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.2011.00732.x"},{"key":"e_1_3_2_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/env.1147"},{"key":"e_1_3_2_49_1","article-title":"\u201cA General Framework for Vecchia Approximations of Gaussian Processes\u201d","author":"Katzfuss M.","year":"2017","unstructured":"Katzfuss, M., and Guinness, J. (2017), \u201cA General Framework for Vecchia Approximations of Gaussian Processes\u201d arXiv preprint: 1708.06302.","journal-title":"arXiv preprint: 1708.06302"},{"key":"e_1_3_2_50_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000002014"},{"key":"e_1_3_2_51_1","article-title":"\u201cThe Big Data Bootstrap,\u201d","author":"Kleiner A.","year":"2012","unstructured":"Kleiner, A., Talwalkar, A., Sarkar, P., and Jordan, M. I. (2012), \u201cThe Big Data Bootstrap,\u201d arXiv preprint: 1206.6415.","journal-title":"arXiv preprint: 1206.6415"},{"key":"e_1_3_2_52_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341x.2000.00013.x"},{"key":"e_1_3_2_53_1","first-page":"202","volume-title":"Proceedings of the Second International Conference on Knowledge Discovery and Data Mining","author":"Kohavi R.","year":"1996","unstructured":"Kohavi, R. (1996), \u201cScaling up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid,\u201d in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202\u2013207."},{"key":"e_1_3_2_54_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2013.812872"},{"key":"e_1_3_2_55_1","first-page":"181","volume-title":"International Conference on Machine Learning","author":"Korattikara A.","year":"2014","unstructured":"Korattikara, A., Chen, Y., and Welling, M. (2014), \u201cAusterity in MCMC Land: Cutting the Metropolis\u2013Hastings Budget,\u201d International Conference on Machine Learning, Beijing, China, 181\u2013189."},{"key":"e_1_3_2_56_1","volume-title":"Analyzing Complex Survey Data","author":"Lee E.","year":"2004","unstructured":"Lee, E., and Forthofer, R. (2004), Analyzing Complex Survey Data, Thousand Oaks, CA: Sage Publications."},{"key":"e_1_3_2_57_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2012.746061"},{"key":"e_1_3_2_58_1","volume-title":"Sampling Design and Analysis","author":"Lohr S.","year":"1999","unstructured":"Lohr, S. (1999), Sampling Design and Analysis, Pacific Grove, CA: Brooks\/Cole Publishing Company."},{"key":"e_1_3_2_59_1","article-title":"\u201cFirefly Monte Carlo: Exact MCMC With Subsets of Data,\u201d","author":"Maclaurin D.","year":"2014","unstructured":"Maclaurin, D., and Adams, R. P. (2014), \u201cFirefly Monte Carlo: Exact MCMC With Subsets of Data,\u201d arXiv: 1403.5693.","journal-title":"arXiv: 1403.5693"},{"key":"e_1_3_2_60_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/37.1-2.17"},{"key":"e_1_3_2_61_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssc.12061"},{"key":"e_1_3_2_62_1","article-title":"\u201cAsymptotically Exact, Embarrassingly Parallel MCMC,\u201d","author":"Neiswanger W.","year":"2013","unstructured":"Neiswanger, W., Wang, C., and Xing, E. (2013), \u201cAsymptotically Exact, Embarrassingly Parallel MCMC,\u201d arXiv preprint: 1311.4780.","journal-title":"arXiv preprint: 1311.4780"},{"key":"e_1_3_2_63_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1448827"},{"key":"e_1_3_2_64_1","article-title":"\u201cThe Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC,\u201d","author":"Quiroz M.","year":"2016","unstructured":"Quiroz, M., Tran, M. N., Villani, M., Kohn, R., and Dang, K. D. (2016), \u201cThe Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC,\u201d arXiv preprint: 1603.08232.","journal-title":"arXiv preprint: 1603.08232"},{"key":"e_1_3_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2391229.2391236"},{"key":"e_1_3_2_66_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.09188"},{"key":"e_1_3_2_67_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780203492024"},{"key":"e_1_3_2_68_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2011.01007.x"},{"key":"e_1_3_2_69_1","first-page":"3111","volume-title":"Proceedings of the Joint Statistical Meetings","author":"Sengupta A.","year":"2012","unstructured":"Sengupta, A., Cressie, N., Frey, R., and Kahn, B. (2012), \u201cStatistical Modeling of MODIS Cloud Data Using the Spatial Random Effects Model,\u201d in Proceedings of the Joint Statistical Meetings, Alexandria, VA: American Statistical Association, pp. 3111\u20133123."},{"key":"e_1_3_2_70_1","first-page":"639","article-title":"\u201cA Constructive Definition of Dirichlet Priors","volume":"4","author":"Sethuraman J.","year":"1994","unstructured":"Sethuraman, J. (1994), \u201cA Constructive Definition of Dirichlet Priors,\u201d Statistica Sinica, 4, 639\u2013650.","journal-title":"Statistica Sinica"},{"key":"e_1_3_2_71_1","doi-asserted-by":"publisher","DOI":"10.1002\/env.864"},{"key":"e_1_3_2_72_1","first-page":"312","article-title":"\u201cScalable Bayes Via Barycenter in Wasserstein Space,\u201d","volume":"19","author":"Srivastava S.","year":"2018","unstructured":"Srivastava, S., Li, C., and Dunson, D. B. (2018), \u201cScalable Bayes Via Barycenter in Wasserstein Space,\u201d The Journal of Machine Learning Research, 19, 312\u2013346.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_73_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2013.06.003"},{"key":"e_1_3_2_74_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1369-7412.2003.05512.x"},{"key":"e_1_3_2_75_1","volume-title":"Studies in Global Econometrics","author":"Theil H.","year":"1996","unstructured":"Theil, H. (1996), Studies in Global Econometrics, Dordrecht, The Netherlands: Kluwer Academic Publishers."},{"key":"e_1_3_2_76_1","doi-asserted-by":"publisher","DOI":"10.1198\/000313007X169037"},{"key":"e_1_3_2_77_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1988.tb01729.x"},{"key":"e_1_3_2_78_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970128"},{"key":"e_1_3_2_79_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420072884-c8"},{"key":"e_1_3_2_80_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/86.4.815"},{"key":"e_1_3_2_81_1","first-page":"1459","article-title":"\u201cEfficient Gaussian Process Modeling Using Experimental Design-Based Subaggin","volume":"28","author":"Zhao Y.","year":"2018","unstructured":"Zhao, Y., Amemiya, Y., and Hung, Y. (2018), \u201cEfficient Gaussian Process Modeling Using Experimental Design-Based Subaggin,\u201d Statistica Sinica, 28, 1459\u20131479.","journal-title":"Statistica Sinica"}],"container-title":["Journal of Computational and Graphical Statistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/10618600.2021.1923518","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T11:14:05Z","timestamp":1720696445000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/10618600.2021.1923518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":80,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,10,2]]}},"alternative-id":["10.1080\/10618600.2021.1923518"],"URL":"https:\/\/doi.org\/10.1080\/10618600.2021.1923518","relation":{},"ISSN":["1061-8600","1537-2715"],"issn-type":[{"value":"1061-8600","type":"print"},{"value":"1537-2715","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,21]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ucgs20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=ucgs20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2019-12-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}