{"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/mixture-density-network-estimation-of","title":"Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples","arxiv_id":"2103.13416","date":"2021-03-24","proceeding":null,"authors":["Charles Burton","Spencer Stubbs","Peter Onyisi"],"abstract":"Mixture Density Networks (MDNs) can be used to generate probability density functions of model parameters $\\boldsymbol{\\theta}$ given a set of observables $\\mathbf{x}$. In some applications, training data are available only for discrete values of a continuous parameter $\\boldsymbol{\\theta}$. In such situations a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.","url_abs":"https://arxiv.org/abs/2103.13416v2","url_pdf":"https://arxiv.org/pdf/2103.13416v2.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":"mixture-density-network-estimation-of","repo_url":"https://github.com/cburton12/MDN_Likelihood_Tutorial","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}