{"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/collaborative-nested-sampling-big-data-vs","title":"Collaborative Nested Sampling: Big Data vs. complex physical models","arxiv_id":"1707.04476","date":"2017-07-14","proceeding":null,"authors":["Johannes Buchner"],"abstract":"The data torrent unleashed by current and upcoming astronomical surveys\ndemands scalable analysis methods. Many machine learning approaches scale well,\nbut separating the instrument measurement from the physical effects of\ninterest, dealing with variable errors, and deriving parameter uncertainties is\noften an after-thought. Classic forward-folding analyses with Markov Chain\nMonte Carlo or Nested Sampling enable parameter estimation and model\ncomparison, even for complex and slow-to-evaluate physical models. However,\nthese approaches require independent runs for each data set, implying an\nunfeasible number of model evaluations in the Big Data regime. Here I present a\nnew algorithm, collaborative nested sampling, for deriving parameter\nprobability distributions for each observation. Importantly, the number of\nphysical model evaluations scales sub-linearly with the number of data sets,\nand no assumptions about homogeneous errors, Gaussianity, the form of the model\nor heterogeneity/completeness of the observations need to be made.\nCollaborative nested sampling has immediate application in speeding up analyses\nof large surveys, integral-field-unit observations, and Monte Carlo\nsimulations.","url_abs":"http://arxiv.org/abs/1707.04476v5","url_pdf":"http://arxiv.org/pdf/1707.04476v5.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":"collaborative-nested-sampling-big-data-vs","repo_url":"https://github.com/JohannesBuchner/massivedatans","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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}