在链内并行-给定 Stan 喷气燃料用于药量测量中的人口建模
1Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA.
CPT: pharmacometrics & systems pharmacology
|October 28, 2024
概括
使用Stan和Torsten可以加速药量学中的贝叶斯数据分析. 链内并行化显著减少了马尔科夫链蒙特卡洛 (MCMC) 采样的计算时间.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 计算统计学 计算统计学
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 斯坦 (Stan) 是一个用于贝叶斯数据分析的概率编程语言.
- 托斯扩展了Stan用于药量计模型,事件处理和ODE系统.
- 马尔科夫链蒙特卡洛 (MCMC) 对于贝叶斯分析至关重要,但计算密集.
研究的目的:
- 为了加快药量计模型在Stan的MCMC采样过程.
- 为了证明链内并行技术的有效性.
主要方法:
- 使用Stan的 reduce_sum () 函数实现了多线程.
- 使用多处理与Torsten的组ODE解决方案用于并行计算.
- 将并行方法与标准的非并行方法进行比较.
主要成果:
- 无论是多线程还是多处理方法,都大大减少了采样时间.
- 链内并行化显著减少了后部分布采样所需的时间.
- 加快采样使贝叶斯方法在药量计学中更频繁地使用.
结论:
- 链内并行化是一种有效的策略,可以加快药量学中的贝叶斯分析.
- 斯坦的 reduce_sum () 和托斯的组 ODE 解决方案为计算挑战提供了实际的解决方案.
- 这些方法使得计算上昂贵的贝叶斯技术对研究人员来说更加可行.
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