Probabilistic Programming in Python using PyMC
Probabilistic Programming in Python using PyMC
复制标题
DOI：
--
复制
发表时间：
2015-07
期刊：
影响因子：
--
通讯作者：
J. Salvatier;Thomas V. Wiecki;C. Fonnesbeck
中科院分区：
文献类型：
--
作者：
J. Salvatier;Thomas V. Wiecki;C. Fonnesbeck
概率编程 (PP) 允许在代码中灵活指定贝叶斯统计模型。 PyMC3 是一个新的开源 PP 框架，具有直观、可读且功能强大的语法，接近统计学家用来描述模型的自然语法。它具有下一代马尔可夫链蒙特卡罗 (MCMC) 采样算法，例如 No-U-Turn 采样器（NUTS；Hoffman，2014）、哈密顿蒙特卡罗（HMC；Duane，1987）的自调整变体。 Python 中的概率编程具有许多优势，包括多平台兼容性、富有表现力且简洁易读的语法、与其他科学库的轻松集成以及通过 C、C++、Fortran 或 Cython 的可扩展性。这些功能使得根据贝叶斯分析的要求编写和使用自定义统计分布、采样器和转换函数变得相对简单。
Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intutive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS; Hoffman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis.