{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T22:44:20Z","timestamp":1760913860230,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032039231","type":"print"},{"value":"9783032039248","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-03924-8_13","type":"book-chapter","created":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T22:03:14Z","timestamp":1760911394000},"page":"125-132","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometric Gaussian Approximations of Probability Distributions"],"prefix":"10.1007","author":[{"given":"Natha\u00ebl","family":"Da Costa","sequence":"first","affiliation":[]},{"given":"B\u00e1lint","family":"Mucs\u00e1nyi","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Hennig","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Ay, N., Jost, J., L\u00ea, H.V., Schwachh\u00f6fer, L.: Information Geometry, Ergebnisse der Mathematik und ihrer Grenzgebiete 34, vol.\u00a064. Springer International Publishing, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-56478-4","DOI":"10.1007\/978-3-319-56478-4"},{"key":"13_CR2","unstructured":"Bergamin, F., Moreno-Mu\u00f1oz, P., Hauberg, S., Arvanitidis, G.: Riemannian Laplace approximations for Bayesian neural networks. In: Proceedings of the 37th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation (2023). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/631f99d8e860054410c239fc90d18270-Paper-Conference.pdf"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Bui, T.D.: Likelihood approximations via Gaussian approximate inference (2024). https:\/\/doi.org\/10.48550\/arXiv.2410.20754","DOI":"10.48550\/arXiv.2410.20754"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"do\u00a0Carmo, M.: Riemannian Geometry. Birkh\u00e4user Boston (2013). https:\/\/link.springer.com\/book\/9780817634902","DOI":"10.1007\/978-1-4757-2201-7_2"},{"key":"13_CR5","unstructured":"Ditlevsen, O., Madsen, H.O.: Structural Reliability Methods. Wiley, Hoboken (1996)"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Hobbhahn, M., Hennig, P.: Laplace matching for fast approximate inference in latent gaussian models (2022). https:\/\/doi.org\/10.48550\/arXiv.2105.03109","DOI":"10.48550\/arXiv.2105.03109"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Kobyzev, I., Prince, S.J., Brubaker, M.A.: Normalizing flows: an introduction and review of current methods. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 3964\u20133979 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2992934. https:\/\/ieeexplore.ieee.org\/document\/9089305.conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2020.2992934"},{"issue":"1","key":"13_CR8","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1023\/A:1007558615313","volume":"33","author":"DJ MacKay","year":"1998","unstructured":"MacKay, D.J.: Choice of basis for Laplace approximation. Mach. Learn. 33(1), 77\u201386 (1998). https:\/\/doi.org\/10.1023\/A:1007558615313","journal-title":"Mach. Learn."},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Mucs\u00e1nyi, B., Da\u00a0Costa, N., Hennig, P.: Rethinking approximate Gaussian inference in classification (2025). https:\/\/doi.org\/10.48550\/arXiv.2502.03366","DOI":"10.48550\/arXiv.2502.03366"},{"key":"13_CR10","unstructured":"Roy, H., Miani, M., Ek, C.H., Hennig, P., Pf\u00f6rtner, M., Tatzel, L., Hauberg, S.: Reparameterization invariance in approximate Bayesian inference (2024). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/0f934dd2030f5740cde0aa2697a105a9-Paper-Conference.pdf"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"Yang, L., et al.: Diffusion models: a comprehensive survey of methods and applications. ACM Comput. Surv. 56(4), 105:1\u2013105:39 (2023). https:\/\/doi.org\/10.1145\/3626235","DOI":"10.1145\/3626235"},{"key":"13_CR12","unstructured":"Yu, H., Hartmann, M., Sanchez, B.W.M., Girolami, M., Klami, A.: Riemannian Laplace approximation with the Fisher metric. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, pp. 820\u2013828. PMLR (2024). https:\/\/proceedings.mlr.press\/v238\/yu24a.html. iSSN: 2640-3498"}],"container-title":["Lecture Notes in Computer Science","Geometric Science of Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-03924-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T22:03:17Z","timestamp":1760911397000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-03924-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"ISBN":["9783032039231","9783032039248"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-03924-8_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,20]]},"assertion":[{"value":"20 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Geometric Science of Information","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Saint-Malo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gsi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference-gsi.org\/gsi-2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}