{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T13:36:26Z","timestamp":1778765786528,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["FT210100260"],"award-info":[{"award-number":["FT210100260"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["FT170106079"],"award-info":[{"award-number":["FT170106079"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015894","name":"Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers","doi-asserted-by":"publisher","award":["CE140100049"],"award-info":[{"award-number":["CE140100049"]}],"id":[{"id":"10.13039\/501100015894","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Stat Comput"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11222-023-10207-5","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T18:15:38Z","timestamp":1674152138000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Modularized Bayesian analyses and cutting feedback in likelihood-free inference"],"prefix":"10.1007","volume":"33","author":[{"given":"Atlanta","family":"Chakraborty","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David J.","family":"Nott","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher C.","family":"Drovandi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David T.","family":"Frazier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott A.","family":"Sisson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"issue":"3","key":"10207_CR1","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.jfineco.2015.03.002","volume":"117","author":"Y A\u00eft-Sahalia","year":"2015","unstructured":"A\u00eft-Sahalia, Y., Cacho-Diaz, J., Laeven, R.J.: Modeling financial contagion using mutually exciting jump processes. J. Financ. Econom. 117(3), 585\u2013606 (2015)","journal-title":"J. Financ. Econom."},{"issue":"3","key":"10207_CR2","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1111\/j.0006-341X.2001.00803.x","volume":"57","author":"J Bennett","year":"2001","unstructured":"Bennett, J., Wakefield, J.: Errors-in-variables in joint population pharmacokinetic\/pharmacodynamic modeling. Biometrics 57(3), 803\u2013812 (2001)","journal-title":"Biometrics"},{"issue":"5","key":"10207_CR3","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1111\/rssb.12158","volume":"78","author":"PG Bissiri","year":"2016","unstructured":"Bissiri, P.G., Holmes, C.C., Walker, S.G.: A general framework for updating belief distributions. J. Royal Stat. Soc.: Series B (Stat. Methodol.) 78(5), 1103\u20131130 (2016)","journal-title":"J. Royal Stat. Soc.: Series B (Stat. Methodol.)"},{"issue":"1","key":"10207_CR4","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1214\/14-BA891","volume":"10","author":"FV Bonassi","year":"2015","unstructured":"Bonassi, F.V., West, M.: Sequential Monte Carlo with adaptive weights for approximate Bayesian computation. Bayesian Anal. 10(1), 171\u2013187 (2015)","journal-title":"Bayesian Anal."},{"key":"10207_CR5","doi-asserted-by":"crossref","unstructured":"Bonassi, F.V., You, L., West, M.: Bayesian learning from marginal data in bionetwork models. Stat. Appl. Genet. Mol. Biol. 10(1), (2011)","DOI":"10.2202\/1544-6115.1684"},{"key":"10207_CR6","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198523963.001.0001","volume-title":"Applied Smoothing Techniques for Data Analysis","author":"AW Bowman","year":"1997","unstructured":"Bowman, A.W., Azzalini, A.: Applied Smoothing Techniques for Data Analysis. Oxford University Press, New York (1997)"},{"key":"10207_CR7","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.jtbi.2017.10.032","volume":"437","author":"AP Browning","year":"2018","unstructured":"Browning, A.P., McCue, S.W., Binny, R.N., et al.: Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data. J. Theor. Biol. 437, 251\u2013260 (2018)","journal-title":"J. Theor. Biol."},{"key":"10207_CR8","unstructured":"Carmona, C., Nicholls, G.: Semi-modular inference: enhanced learning in multi-modular models by tempering the influence of components. In: Chiappa S, Calandra R (eds) Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol 108. PMLR, pp 4226\u20134235, (2020)"},{"key":"10207_CR9","unstructured":"Carmona, C., Nicholls, G.: Scalable semi-modular inference with variational meta-posteriors, (2022). arXiv:2204.00296"},{"key":"10207_CR10","doi-asserted-by":"crossref","unstructured":"Chakraborty, A., Nott, D.J., Evans, M.: Weakly informative priors and prior-data conflict checking for likelihood-free inference. Stat. Interface, To appear, (2023)","DOI":"10.4310\/22-SII733"},{"issue":"3","key":"10207_CR11","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1093\/biomet\/asaa090","volume":"108","author":"G Clart\u00e9","year":"2020","unstructured":"Clart\u00e9, G., Robert, C.P., Ryder, R.J., et al.: Componentwise approximate Bayesian computation via Gibbs-like steps. Biometrika 108(3), 591\u2013607 (2020)","journal-title":"Biometrika"},{"key":"10207_CR12","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.jempfin.2015.01.002","volume":"31","author":"M Creel","year":"2015","unstructured":"Creel, M., Kristensen, D.: ABC of SV: limited information likelihood inference in stochastic volatility jump-diffusion models. J. Empir. Financ. 31, 85\u2013108 (2015)","journal-title":"J. Empir. Financ."},{"issue":"1","key":"10207_CR13","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1002\/sta4.15","volume":"2","author":"Y Fan","year":"2013","unstructured":"Fan, Y., Nott, D.J., Sisson, S.A.: Approximate Bayesian computation via regression density estimation. Stat 2(1), 34\u201348 (2013)","journal-title":"Stat"},{"key":"10207_CR14","unstructured":"Forbes, F., Nguyen, H.D., Nguyen, T.T. et\u00a0al.: Approximate Bayesian computation with surrogate posteriors. Inria technical report, hal-03139256, (2021), https:\/\/hal.archives-ouvertes.fr\/hal-03139256v2\/file\/Gllim-ABC_v2_4HALApril2021.pdf"},{"issue":"4","key":"10207_CR15","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1080\/10618600.2021.1875839","volume":"30","author":"DT Frazier","year":"2021","unstructured":"Frazier, D.T., Drovandi, C.: Robust approximate Bayesian inference with synthetic likelihood. J. Comput. Graph. Stat. 30(4), 958\u2013976 (2021)","journal-title":"J. Comput. Graph. Stat."},{"key":"10207_CR16","unstructured":"Frazier, D.T., Nott, D.J.: Cutting feedback and modularized analyses in generalized bayesian inference, (2022). arXiv:2202.09968"},{"issue":"1","key":"10207_CR17","doi-asserted-by":"publisher","first-page":"113","DOI":"10.3982\/QE986","volume":"11","author":"DT Frazier","year":"2020","unstructured":"Frazier, D.T., Renault, E.: Indirect inference with (out) constraints. Quant. Econ. 11(1), 113\u2013159 (2020)","journal-title":"Quant. Econ."},{"issue":"2","key":"10207_CR18","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.ijforecast.2018.08.003","volume":"35","author":"DT Frazier","year":"2019","unstructured":"Frazier, D.T., Maneesoonthorn, W., Martin, G.M., et al.: Approximate Bayesian forecasting. Int. J. Forecast. 35(2), 521\u2013539 (2019)","journal-title":"Int. J. Forecast."},{"issue":"2","key":"10207_CR19","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1111\/rssb.12356","volume":"82","author":"DT Frazier","year":"2020","unstructured":"Frazier, D.T., Robert, C.P., Rousseau, J.: Model misspecification in approximate Bayesian computation: consequences and diagnostics. J. Royal Stat. Soc.: Series B (Stat. Methodol.) 82(2), 421\u2013444 (2020)","journal-title":"J. Royal Stat. Soc.: Series B (Stat. Methodol.)"},{"key":"10207_CR20","unstructured":"Frazier, D.T., Drovandi, C., Nott, D.J.: Synthetic likelihood in misspecified models: Consequences and corrections, (2021). arXiv preprint arXiv:2104.03436"},{"issue":"2","key":"10207_CR21","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1049\/ip-f-2.1993.0015","volume":"140","author":"N Gordon","year":"1993","unstructured":"Gordon, N., Salmond, D., Smith, A.: Novel approach to nonlinear\/non-Gaussian Bayesian state estimation. IEE Proc. F (Radar Signal Proc.) 140(2), 107\u2013113 (1993)","journal-title":"IEE Proc. F (Radar Signal Proc.)"},{"key":"10207_CR22","unstructured":"Greenberg, D.S., Nonnenmacher, M., Macke, J.H.: Automatic posterior transformation for likelihood-free inference. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Proceedings of Machine Learning Research, vol\u00a097. PMLR, pp 2404\u20132414, (2019)"},{"issue":"4","key":"10207_CR23","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1214\/17-BA1085","volume":"12","author":"P Gr\u00fcnwald","year":"2017","unstructured":"Gr\u00fcnwald, P., van Ommen, T.: Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it. Bayesian Anal. 12(4), 1069\u20131103 (2017)","journal-title":"Bayesian Anal."},{"issue":"125","key":"10207_CR24","first-page":"1","volume":"17","author":"MU Gutmann","year":"2016","unstructured":"Gutmann, M.U., Corander, J.: Bayesian optimization for likelihood-free inference of simulator-based statistical models. J. Mach. Learn. Res. 17(125), 1\u201347 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"10207_CR25","unstructured":"He, Z., Huo, S., Yang, T.: An adaptive mixture-population Monte Carlo method for likelihood-free inference, (2021). arXiv:2112.00420"},{"key":"10207_CR26","unstructured":"Hermans, J., Begy, V., Louppe, G.: Likelihood-free MCMC with amortized approximate ratio estimators. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research, vol 119. PMLR, pp 4239\u20134248, (2020)"},{"key":"10207_CR27","doi-asserted-by":"crossref","unstructured":"Hershey, J.R., Olsen, P.A.: Approximating the kullback leibler divergence between gaussian mixture models. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP \u201907, pp IV\u2013317\u2013IV\u2013320, (2007)","DOI":"10.1109\/ICASSP.2007.366913"},{"key":"10207_CR28","unstructured":"Jacob, P.E., Murray, L.M., Holmes, C.C., et\u00a0al.: Better together? Statistical learning in models made of modules, (2017). arXiv:1708.08719"},{"issue":"3","key":"10207_CR29","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1111\/rssb.12336","volume":"82","author":"PE Jacob","year":"2020","unstructured":"Jacob, P.E., O\u2019Leary, J., Atchad\u00e9, Y.F.: Unbiased Markov chain Monte Carlo methods with couplings (with discussion). J. Royal Stat. Soc.: Series B (Stat. Methodol.) 82(3), 543\u2013600 (2020)","journal-title":"J. Royal Stat. Soc.: Series B (Stat. Methodol.)"},{"issue":"4","key":"10207_CR30","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1214\/21-BA1257","volume":"16","author":"JR Lewis","year":"2021","unstructured":"Lewis, J.R., MacEachern, S.N., Lee, Y.: Bayesian restricted likelihood methods: conditioning on insufficient statistics in Bayesian regression. Bayesian Anal. 16(4), 1393\u20131462 (2021)","journal-title":"Bayesian Anal."},{"key":"10207_CR31","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.csda.2016.07.005","volume":"106","author":"J Li","year":"2017","unstructured":"Li, J., Nott, D.J., Fan, Y., et al.: Extending approximate Bayesian computation methods to high dimensions via Gaussian copula. Comput. Stat. Data Anal. 106, 77\u201389 (2017)","journal-title":"Comput. Stat. Data Anal."},{"issue":"1","key":"10207_CR32","first-page":"119","volume":"4","author":"F Liu","year":"2009","unstructured":"Liu, F., Bayarri, M.J., Berger, J.O.: Modularization in Bayesian analysis, with emphasis on analysis of computer models. Bayesian Anal. 4(1), 119\u2013150 (2009)","journal-title":"Bayesian Anal."},{"key":"10207_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Y., Goudie, R.J.B.: Stochastic approximation cut algorithm for inference in modularized Bayesian models, (2020). arXiv:2006.01584","DOI":"10.1007\/s11222-021-10070-2"},{"key":"10207_CR34","unstructured":"Liu, Y., Goudie, R.J.B.: A general framework for cutting feedback within modularized Bayesian inference, (2022). arXiv:2211.03274"},{"key":"10207_CR35","unstructured":"Lueckmann, J.M., Goncalves, P.J., Bassetto, G., et\u00a0al.: Flexible statistical inference for mechanistic models of neural dynamics. In: Guyon I, Luxburg UV, Bengio S, et\u00a0al (eds) Advances in Neural Information Processing Systems, vol\u00a030. Curran Associates, Inc., (2017), https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/addfa9b7e234254d26e9c7f2af1005cb-Paper.pdf"},{"key":"10207_CR36","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10928-008-9109-1","volume":"36","author":"D Lunn","year":"2009","unstructured":"Lunn, D., Best, N., Spiegelhalter, D., et al.: Combining MCMC with \u2018sequential\u2019 PKPD modelling. J. Pharmacokinet Pharmacodyn. 36, 19\u201338 (2009)","journal-title":"J. Pharmacokinet Pharmacodyn."},{"issue":"3","key":"10207_CR37","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1002\/jae.2547","volume":"32","author":"W Maneesoonthorn","year":"2017","unstructured":"Maneesoonthorn, W., Forbes, C.S., Martin, G.M.: Inference on self-exciting jumps in prices and volatility using high-frequency measures. J. Appl. Economet. 32(3), 504\u2013532 (2017)","journal-title":"J. Appl. Economet."},{"issue":"527","key":"10207_CR38","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1080\/01621459.2018.1469995","volume":"114","author":"JW Miller","year":"2019","unstructured":"Miller, J.W., Dunson, D.B.: Robust Bayesian inference via coarsening. J. Am. Stat. Assoc. 114(527), 1113\u20131125 (2019)","journal-title":"J. Am. Stat. Assoc."},{"key":"10207_CR39","unstructured":"Nicholls, G.K., Lee, J.E., Wu, C.H., et\u00a0al.: Valid belief updates for prequentially additive loss functions arising in semi-modular inference, (2022). arXiv preprint arXiv:2201.09706 )"},{"key":"10207_CR40","doi-asserted-by":"crossref","unstructured":"Nott, D.J., Wang, X., Evans, M., et al.: Checking for prior-data conflict using prior-to-posterior divergences. Stat. Sci. 35(2), 234\u2013253 (2020","DOI":"10.1214\/19-STS731"},{"key":"10207_CR41","unstructured":"Pacchiardi, L., Dutta, R.: Generalized Bayesian likelihood-free inference using scoring rules estimators, (2021). arXiv:2104.03889"},{"issue":"38","key":"10207_CR42","first-page":"1","volume":"23","author":"L Pacchiardi","year":"2022","unstructured":"Pacchiardi, L., Dutta, R.: Score matched neural exponential families for likelihood-free inference. J. Mach. Learn. Res. 23(38), 1\u201371 (2022)","journal-title":"J. Mach. Learn. Res."},{"key":"10207_CR43","unstructured":"Papamakarios, G., Murray, I.: Fast $$\\epsilon $$-free inference of simulation models with Bayesian conditional density estimation. In: Lee D, Sugiyama M, Luxburg U, et\u00a0al (eds) Advances in Neural Information Processing Systems, vol\u00a029. Curran Associates, Inc., (2016), https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/6aca97005c68f1206823815f66102863-Paper.pdf"},{"key":"10207_CR44","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s11222-014-9503-z","volume":"25","author":"M Plummer","year":"2015","unstructured":"Plummer, M.: Cuts in Bayesian graphical models. Stat. Comput. 25, 37\u201343 (2015)","journal-title":"Stat. Comput."},{"key":"10207_CR45","unstructured":"Pompe, E., Jacob, P.E.: Asymptotics of cut distributions and robust modular inference using posterior bootstrap, (2021). arXiv:2110.11149"},{"issue":"1","key":"10207_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10618600.2017.1302882","volume":"27","author":"LF Price","year":"2018","unstructured":"Price, L.F., Drovandi, C.C., Lee, A.C., et al.: Bayesian synthetic likelihood. J. Comput. Graph. Stat. 27(1), 1\u201311 (2018)","journal-title":"J. Comput. Graph. Stat."},{"issue":"10","key":"10207_CR47","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1093\/bioinformatics\/bty867","volume":"35","author":"L Raynal","year":"2018","unstructured":"Raynal, L., Marin, J.M., Pudlo, P., et al.: ABC random forests for Bayesian parameter inference. Bioinformatics 35(10), 1720\u20131728 (2018)","journal-title":"Bioinformatics"},{"key":"10207_CR48","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1007\/s11222-020-09933-x","volume":"30","author":"G Rodrigues","year":"2020","unstructured":"Rodrigues, G., Nott, D., Sisson, S.: Likelihood-free approximate Gibbs sampling. Stat. Comput. 30, 1057\u20131073 (2020)","journal-title":"Stat. Comput."},{"issue":"1","key":"10207_CR49","doi-asserted-by":"publisher","first-page":"289","DOI":"10.32614\/RJ-2016-021","volume":"8","author":"L Scrucca","year":"2016","unstructured":"Scrucca, L., Fop, M., Murphy, T.B., et al.: mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal 8(1), 289\u2013317 (2016)","journal-title":"The R Journal"},{"key":"10207_CR50","doi-asserted-by":"crossref","unstructured":"Sisson, S., Fan, Y., Beaumont, M.: Overview of Approximate Bayesian Computation. In: Sisson S, Fan Y, Beaumont M (eds) Handbook of Approximate Bayesian Computation. Chapman & Hall\/CRC Handbooks of Modern Statistical Methods, CRC Press, Taylor & Francis Group, Boca Raton, Florida, chap\u00a01, (2018a)","DOI":"10.1201\/9781315117195"},{"key":"10207_CR51","doi-asserted-by":"crossref","unstructured":"Sisson, S.A., Fan, Y., Beaumont, M.A.: (eds) Handbook of Approximate Bayesian Computation. Chapman & Hall\/CRC, (2018b)","DOI":"10.1201\/9781315117195"},{"issue":"4","key":"10207_CR52","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1214\/aoms\/1177704873","volume":"32","author":"M Stone","year":"1961","unstructured":"Stone, M.: The Opinion Pool. Ann. Math. Stat. 32(4), 1339\u20131342 (1961)","journal-title":"Ann. Math. Stat."},{"issue":"1","key":"10207_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/20-BA1238","volume":"17","author":"O Thomas","year":"2022","unstructured":"Thomas, O., Dutta, R., Corander, J., et al.: Likelihood-free inference by ratio estimation. Bayesian Anal. 17(1), 1\u201331 (2022)","journal-title":"Bayesian Anal."},{"issue":"2","key":"10207_CR54","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1515\/sagmb-2013-0010","volume":"12","author":"RD Wilkinson","year":"2013","unstructured":"Wilkinson, R.D.: Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Stat. Appl. Genet. Mol. Biol. 12(2), 129\u2013141 (2013)","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"issue":"7310","key":"10207_CR55","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1038\/nature09319","volume":"466","author":"SN Wood","year":"2010","unstructured":"Wood, S.N.: Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466(7310), 1102\u20131104 (2010)","journal-title":"Nature"},{"issue":"4","key":"10207_CR56","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1080\/10618600.2012.694765","volume":"22","author":"DB Woodard","year":"2013","unstructured":"Woodard, D.B., Crainiceanu, C., Ruppert, D.: Hierarchical adaptive regression kernels for regression with functional predictors. J. Comput. Graph. Stat. 22(4), 777\u2013800 (2013)","journal-title":"J. Comput. Graph. Stat."},{"key":"10207_CR57","unstructured":"Yu, X., Nott, D.J., Smith, M.S.: Variational inference for cutting feedback in misspecified models, (2021). arXiv:2108.11066"}],"container-title":["Statistics and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-023-10207-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11222-023-10207-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11222-023-10207-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T02:00:40Z","timestamp":1701741640000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11222-023-10207-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,19]]},"references-count":57,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["10207"],"URL":"https:\/\/doi.org\/10.1007\/s11222-023-10207-5","relation":{},"ISSN":["0960-3174","1573-1375"],"issn-type":[{"value":"0960-3174","type":"print"},{"value":"1573-1375","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,19]]},"assertion":[{"value":"2 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"33"}}