{"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/abc-cde-towards-approximate-bayesian","title":"ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations","arxiv_id":"1805.05480","date":"2018-05-14","proceeding":null,"authors":["Rafael Izbicki","Ann B. Lee","Taylor Pospisil"],"abstract":"Approximate Bayesian Computation (ABC) is typically used when the likelihood\nis either unavailable or intractable but where data can be simulated under\ndifferent parameter settings using a forward model. Despite the recent interest\nin ABC, high-dimensional data and costly simulations still remain a bottleneck\nin some applications. There is also no consensus as to how to best assess the\nperformance of such methods without knowing the true posterior. We show how a\nnonparametric conditional density estimation (CDE) framework, which we refer to\nas ABC-CDE, help address three nontrivial challenges in ABC: (i) how to\nefficiently estimate the posterior distribution with limited simulations and\ndifferent types of data, (ii) how to tune and compare the performance of ABC\nand related methods in estimating the posterior itself, rather than just\ncertain properties of the density, and (iii) how to efficiently choose among a\nlarge set of summary statistics based on a CDE surrogate loss. We provide\ntheoretical and empirical evidence that justify ABC-CDE procedures that {\\em\ndirectly} estimate and assess the posterior based on an initial ABC sample, and\nwe describe settings where standard ABC and regression-based approaches are\ninadequate.","url_abs":"http://arxiv.org/abs/1805.05480v2","url_pdf":"http://arxiv.org/pdf/1805.05480v2.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":"abc-cde-towards-approximate-bayesian","repo_url":"https://github.com/tpospisi/NNKCDE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.05480","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}