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超越贝叶斯推理:关联整体概率框架和渐变流量方法用于确定性采样
Piotr Gwiazda1,2, Alexey Kazarnikov3, Anna Marciniak-Czochra4
1Institute of Mathematics, Polish Academy of Sciences, ul. Śniadeckich 8, 00-956, Warsaw, Poland.
Bulletin of mathematical biology
|December 24, 2025
概括
本研究引入了相关性整合概率 (CIL) 方法,用于校准复杂的生物模型. CIL方法增强了部分微分方程 (PDE) 模型的参数推理,提高了对噪音数据的预测准确性.
科学领域:
- 数学生物学 数学生物学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 校准生物过程的数学模型对于预测准确性和机械洞察力至关重要.
- 挑战包括有限/杂的数据,生物变异性和模型计算复杂性.
- 在部分微分方程 (PDE) 模型中,参数推理特别困难.
研究的目的:
- 引入一个统一的数学框架,用于生物系统中的参数推理.
- 解决与异质或混乱动态的复杂生物模型校准的挑战.
- 为研究人员提供实用和理论基础的工具箱.
主要方法:
- 介绍了相关性积分概率 (CIL) 方法.
- 开发一个统一的数学框架,用于参数估计.
- 随机抽样 (例如,马尔科夫链蒙特卡罗) 与确定性梯度流程方法的比较.
主要成果:
- 该CIL方法是多功能和适用于模式形成和基于个人的模型.
- 证明CIL方法在数学生物学应用中的实用性.
- 用随机和决定方法的整合策略来提高推理性能.
结论:
- 在复杂的生物模型中,CIL方法为参数推理提供了一个强大的方法.
- 该框架可使用不完整,杂或异质数据进行模型校准.
- 这项工作推进了数学生物学中的预测能力和机械学理解.
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