{"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/denmarf-a-python-package-for-density","title":"denmarf: a Python package for density estimation using masked autoregressive flow","arxiv_id":"2305.14379","date":"2023-05-22","proceeding":null,"authors":["Rico K. L. Lo"],"abstract":"Masked autoregressive flow (MAF) is a state-of-the-art non-parametric density estimation technique. It is based on the idea (known as a normalizing flow) that a simple base probability distribution can be mapped into a complicated target distribution that one wishes to approximate, using a sequence of bijective transformations. The denmarf package provides a scikit-learn-like interface in Python for researchers to effortlessly use MAF for density estimation in their applications to evaluate probability densities of the underlying distribution of a set of data and generate new samples from the data, on either a CPU or a GPU, as simple as \"from denmarf import DensityEstimate; de = DensityEstimate().fit(X)\". The package also implements logistic transformations to facilitate the fitting of bounded distributions.","url_abs":"https://arxiv.org/abs/2305.14379v1","url_pdf":"https://arxiv.org/pdf/2305.14379v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"denmarf-a-python-package-for-density","repo_url":"https://github.com/ricokaloklo/denmarf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}