Papers › FunMC: A functional API for building Markov Chains
FunMC: A functional API for building Markov Chains
Pavel Sountsov, Alexey Radul, Srinivas Vasudevan
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.
Constant-memory algorithms, also loosely called Markov chains, power the vast majority of probabilistic inference and machine learning applications today. A lot of progress has been made in constructing user-friendly APIs around these algorithms. Such APIs, however, rarely make it easy to research new algorithms of this type. In this work we present FunMC, a minimal Python library for doing methodological research into algorithms based on Markov chains. FunMC is not targeted toward data scientists or others who wish to use MCMC or optimization as a black box, but rather towards researchers implementing new Markovian algorithms from scratch.
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