论文标题
TFP.MCMC：现代马尔可夫链蒙特卡洛工具用于现代硬件
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
论文作者
论文摘要
马尔可夫链蒙特卡洛（MCMC）被广泛认为是20世纪最重要的算法之一。仅使用非规范概率函数的渐近收敛，稳定性和估计量变化界限的保证使其对于概率编程必不可少。在本文中，我们介绍了TensorFlow概率MCMC工具包，并讨论了激励其设计的一些考虑因素。
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century. Its guarantees of asymptotic convergence, stability, and estimator-variance bounds using only unnormalized probability functions make it indispensable to probabilistic programming. In this paper, we introduce the TensorFlow Probability MCMC toolkit, and discuss some of the considerations that motivated its design.
