{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/amortized-bayesian-inference-for-clustering","title":"Amortized Bayesian inference for clustering models","arxiv_id":"1811.09747","date":"2018-11-24","proceeding":null,"authors":["Ari Pakman","Liam Paninski"],"abstract":"We develop methods for efficient amortized approximate Bayesian inference\nover posterior distributions of probabilistic clustering models, such as\nDirichlet process mixture models. The approach is based on mapping distributed,\nsymmetry-invariant representations of cluster arrangements into conditional\nprobabilities. The method parallelizes easily, yields iid samples from the\napproximate posterior of cluster assignments with the same computational cost\nof a single Gibbs sampler sweep, and can easily be applied to both conjugate\nand non-conjugate models, as training only requires samples from the generative\nmodel.","url_abs":"http://arxiv.org/abs/1811.09747v1","url_pdf":"http://arxiv.org/pdf/1811.09747v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"amortized-bayesian-inference-for-clustering","repo_url":"https://github.com/aripakman/neural_clustering_process","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.09747","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}