{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T10:05:02Z","timestamp":1790676302166,"version":"4.1.0"},"reference-count":59,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DMS-2515542"],"award-info":[{"award-number":["DMS-2515542"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DMS-2210717"],"award-info":[{"award-number":["DMS-2210717"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We investigate the problem of sampling from posterior distributions with intractable normalizing constants in Bayesian inference. Building on transport map-based generative models for posterior sampling, we propose an optimal transport (OT)-constrained transport map class that learns a deterministic map from a reference distribution to the target posterior through constrained optimization. The proposed class exploits structural properties of OT maps and allows efficient generation of many independent, high-quality posterior samples. The framework supports both continuous and mixed discrete\u2013continuous parameter spaces, with specific adaptations for latent variable models and near-Gaussian posteriors. Beyond computational benefits, it also enables new inferential tools based on OT-derived multivariate ranks and quantiles for Bayesian exploratory analysis and visualization. We demonstrate the effectiveness of our approach through multiple simulation studies and a real-world data analysis.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkag126","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:34:58Z","timestamp":1788521698000},"source":"Crossref","is-referenced-by-count":0,"title":["An optimal transport-based generative model for Bayesian posterior sampling"],"prefix":"10.1093","author":[{"given":"Ke","family":"Li","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Illinois at Urbana-Champaign , Champaign, IL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Illinois at Urbana-Champaign , Champaign, IL 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