可扩展梯度使哈密尔顿式蒙特卡洛采样能够在情节性出生-死亡-采样模型下进行植物动力学推断
Yucai Shao1, Andrew F Magee2, Tetyana I Vasylyeva3,4
1Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, United States.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
我们开发了一个新的线性时间算法,用于插曲性出生死亡采样 (EBDS) 模型,显著提高了植物动力学分析中的计算效率. 这种方法提高了从病原体进化数据中推断流行病学参数的速度.
科学领域:
- 植物动力学是关于植物动力学的.
- 流行病学 流行病学
- 计算生物学 计算生物学
背景情况:
- 出生死亡模型对于从家族遗传树了解流行病学参数至关重要.
- 插曲性出生死亡采样 (EBDS) 模型捕捉了不断变化的传播动态,但面临着计算挑战.
- 目前的推理方法对于使用复杂模型进行大规模的植物动力学分析是无效的.
结论:
- 这种高效的基于HMC的推断方法能够对复杂的EBDS过程进行强大的植物动力学分析.
- 该方法允许灵活的先前选择,并准确推断不断变化的流行病学参数.
- 促进大规模的植物动力学研究和更深入地了解病原体进化和传播.
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