论文标题
FUNMC：用于建造马尔可夫连锁店的功能性API
FunMC: A functional API for building Markov Chains
论文作者
论文摘要
恒定内存算法，也很松散地称为马尔可夫链，为当今的绝大多数概率推理和机器学习应用程序提供动力。围绕这些算法构建用户友好的API，已经取得了很多进展。但是，这种API很少使研究这种类型的新算法变得容易。在这项工作中，我们提出了FunMC，这是一个最小的Python库，用于基于马尔可夫链进行算法进行方法论研究。 FUNMC不是针对数据科学家或希望使用MCMC或优化作为黑匣子的其他人的目标，而是针对从头开始实施新的Markovian算法的研究人员。
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.
