{"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/mixest-an-estimation-toolbox-for-mixture","title":"MixEst: An Estimation Toolbox for Mixture Models","arxiv_id":"1507.06065","date":"2015-07-22","proceeding":null,"authors":["Reshad Hosseini","Mohamadreza Mash'al"],"abstract":"Mixture models are powerful statistical models used in many applications\nranging from density estimation to clustering and classification. When dealing\nwith mixture models, there are many issues that the experimenter should be\naware of and needs to solve. The MixEst toolbox is a powerful and user-friendly\npackage for MATLAB that implements several state-of-the-art approaches to\naddress these problems. Additionally, MixEst gives the possibility of using\nmanifold optimization for fitting the density model, a feature specific to this\ntoolbox. MixEst simplifies using and integration of mixture models in\nstatistical models and applications. For developing mixture models of new\ndensities, the user just needs to provide a few functions for that statistical\ndistribution and the toolbox takes care of all the issues regarding mixture\nmodels. MixEst is available at visionlab.ut.ac.ir/mixest and is fully\ndocumented and is licensed under GPL.","url_abs":"http://arxiv.org/abs/1507.06065v1","url_pdf":"http://arxiv.org/pdf/1507.06065v1.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":"mixest-an-estimation-toolbox-for-mixture","repo_url":"https://github.com/utvisionlab/mixest","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"riemannian-optimization","task_name":"Riemannian optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}