Papers › denmarf: a Python package for density estimation using masked autoregressive flow
denmarf: a Python package for density estimation using masked autoregressive flow
Rico K. L. Lo
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections