{"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/sgmcmc-an-r-package-for-stochastic-gradient","title":"sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo","arxiv_id":"1710.00578","date":"2017-10-02","proceeding":null,"authors":["Jack Baker","Paul Fearnhead","Emily B. Fox","Christopher Nemeth"],"abstract":"This paper introduces the R package sgmcmc; which can be used for Bayesian\ninference on problems with large datasets using stochastic gradient Markov\nchain Monte Carlo (SGMCMC). Traditional Markov chain Monte Carlo (MCMC)\nmethods, such as Metropolis-Hastings, are known to run prohibitively slowly as\nthe dataset size increases. SGMCMC solves this issue by only using a subset of\ndata at each iteration. SGMCMC requires calculating gradients of the log\nlikelihood and log priors, which can be time consuming and error prone to\nperform by hand. The sgmcmc package calculates these gradients itself using\nautomatic differentiation, making the implementation of these methods much\neasier. To do this, the package uses the software library TensorFlow, which has\na variety of statistical distributions and mathematical operations as standard,\nmeaning a wide class of models can be built using this framework. SGMCMC has\nbecome widely adopted in the machine learning literature, but less so in the\nstatistics community. We believe this may be partly due to lack of software;\nthis package aims to bridge this gap.","url_abs":"http://arxiv.org/abs/1710.00578v3","url_pdf":"http://arxiv.org/pdf/1710.00578v3.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":"sgmcmc-an-r-package-for-stochastic-gradient","repo_url":"https://github.com/chris-nemeth/sgmcmc-review-paper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}