个人资料分析:基于个人资料概率的工作流程,用于使用机械数学模型进行可识别性分析,估计和预测
Matthew J Simpson1, Oliver J Maclaren2
1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
本研究介绍了Profile-Wise Analysis (PWA),这是一个统一的工作流程,用于用机械数学模型解释数据. 通过有效地整合可识别性,参数估计和科学见解的预测,PWA增强了发现.
科学领域:
- 计算生物学 计算生物学
- 数学建模的数学建模
- 数据解释 数据解释
背景情况:
- 机械数学模型对于科学发现和决策至关重要.
- 将实验数据与模型相结合,对于推进生物学理解至关重要.
- 关键的挑战包括模型识别,参数估计和预测.
研究的目的:
- 呈现一个系统和计算高效的工作流程,PWA (Profile-Wise Analysis),用于使用机械数学模型解释数据.
- 在一个框架内统一识别性分析,参数估计和模型预测.
- 为了利用近期在概况智能预测间隔方面的进步,进行增强的模型分析.
主要方法:
- 开发和实施了PWA工作流程.
- 利用配置智能预测间隔将参数信心集与模型预测联系起来.
- 扩展了对二维参数的配置智能预测间隔,并为整体预测准确性组合了可信度集.
- 使用高斯和非高斯噪声的普通微分方程 (ODE) 模型演示了工作流.
主要成果:
- PWA提供了一种统一的方法,用于基于机械模型的数据解释的关键步骤.
- 简介明智的方法有效地将参数不确定性传播到预测中.
- 工作流显示了完全基于概率的预测信心集的良好近似.
- 案例研究展示了ODE模型的实际应用.
结论:
- 基于个人资料的分析 (PWA) 为基于机械模型的数据解释提供了一种强大而有效的方法.
- 工作流适用于各种数学模型,包括ODEs,PDE和随机模型.
- 有开源软件可用于促进PWA工作流程的应用和复制.
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