{"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/amidst-a-java-toolbox-for-scalable","title":"AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning","arxiv_id":"1704.01427","date":"2017-04-04","proceeding":null,"authors":["Andrés R. Masegosa","Ana M. Martínez","Darío Ramos-López","Rafael Cabañas","Antonio Salmerón","Thomas D. Nielsen","Helge Langseth","Anders L. Madsen"],"abstract":"The AMIDST Toolbox is a software for scalable probabilistic machine learning\nwith a spe- cial focus on (massive) streaming data. The toolbox supports a\nflexible modeling language based on probabilistic graphical models with latent\nvariables and temporal dependencies. The specified models can be learnt from\nlarge data sets using parallel or distributed implementa- tions of Bayesian\nlearning algorithms for either streaming or batch data. These algorithms are\nbased on a flexible variational message passing scheme, which supports discrete\nand continu- ous variables from a wide range of probability distributions.\nAMIDST also leverages existing functionality and algorithms by interfacing to\nsoftware tools such as Flink, Spark, MOA, Weka, R and HUGIN. AMIDST is an open\nsource toolbox written in Java and available at http://www.amidsttoolbox.com\nunder the Apache Software License version 2.0.","url_abs":"http://arxiv.org/abs/1704.01427v1","url_pdf":"http://arxiv.org/pdf/1704.01427v1.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":"amidst-a-java-toolbox-for-scalable","repo_url":"https://github.com/amidst/jss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}