一个关于模型错误规范和可识别性的警告故事
Alexander P Browning1, Jennifer A Flegg2, Ryan J Murphy3
1School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia. apbrowning@unimelb.edu.au.
简化复杂的生物模型可能会导致不准确的参数估计. 对结构不确定性的计算提高了模型的准确性,并量化了数学生物学中剩余的不确定性.
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:
- 数学模型对于解释生物数据至关重要,有助于预测和参数估计.
- 具有有限数据的复杂和不可识别的模型在数学生物学中提出了重大挑战.
- 模型简化可识别性可以矛盾地引入错误规范并降低准确性.
研究的目的:
- 为了证明结构不确定性如何传播到数学生物学中的参数估计.
- 探索模型识别,错误规范和准确性之间的权衡.
- 提出一种方法来从模型不确定性中划分感兴趣的参数.
主要方法:
- 利用半参数高斯过程方法来量化结构不确定性.
- 将该方法应用于具有未知拥挤函数的通用后勤增长模型.
- 研究了一个空间解析的部分微分方程模型,具有时间依赖的扩散性.
主要成果:
- 考虑到结构模型的不确定性导致了更强大和更准确的参数估计.
- 该方法提供了一个更好的量化模型中剩余的不确定性.
- 证明了简化模型可以导致灾难性的准确性成本.
结论:
- 结构不确定性是复杂生物模型参数估计的关键因素.
- 通过不确定性量化来解决模型错误规范,可以提高预测能力.
- 拟议的高斯过程方法为分析有限数据的生物系统提供了强大的替代方案.
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