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Through a parameter recovery test, we demonstrate that our model makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches.<\/jats:p>","DOI":"10.1088\/2632-2153\/ade114","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T22:55:46Z","timestamp":1749077746000},"page":"025063","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning from topology: cosmological parameter estimation from the large-scale structure"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1921-524X","authenticated-orcid":true,"given":"Jacky H T","family":"Yip","sequence":"first","affiliation":[]},{"given":"Adam","family":"Rouhiainen","sequence":"additional","affiliation":[]},{"given":"Gary","family":"Shiu","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"article-title":"Center for High Throughput Computing. 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