{"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/fast-free-inference-of-simulation-models-with","title":"Fast $ε$-free Inference of Simulation Models with Bayesian Conditional Density Estimation","arxiv_id":"1605.06376","date":"2016-05-20","proceeding":null,"authors":["George Papamakarios","Iain Murray"],"abstract":"Many statistical models can be simulated forwards but have intractable\nlikelihoods. Approximate Bayesian Computation (ABC) methods are used to infer\nproperties of these models from data. Traditionally these methods approximate\nthe posterior over parameters by conditioning on data being inside an\n$\\epsilon$-ball around the observed data, which is only correct in the limit\n$\\epsilon\\!\\rightarrow\\!0$. Monte Carlo methods can then draw samples from the\napproximate posterior to approximate predictions or error bars on parameters.\nThese algorithms critically slow down as $\\epsilon\\!\\rightarrow\\!0$, and in\npractice draw samples from a broader distribution than the posterior. We\npropose a new approach to likelihood-free inference based on Bayesian\nconditional density estimation. Preliminary inferences based on limited\nsimulation data are used to guide later simulations. In some cases, learning an\naccurate parametric representation of the entire true posterior distribution\nrequires fewer model simulations than Monte Carlo ABC methods need to produce a\nsingle sample from an approximate posterior.","url_abs":"http://arxiv.org/abs/1605.06376v4","url_pdf":"http://arxiv.org/pdf/1605.06376v4.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":"fast-free-inference-of-simulation-models-with","repo_url":"https://github.com/gpapamak/epsilon_free_inference","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.06376","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}