对于多变量延迟微分方程模型的最大概率推理
Ahmed Adly Mahmoud1, Abdalla Rabie1, Sarat Chandra Dass2
1Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut, 71524, Egypt.
为一般延迟微分方程模型开发了一个新的最大概率推理框架. 这种方法在没有限制性假设的情况下处理多个延迟参数,为复杂系统推进统计建模.
科学领域:
- 数学建模的数学建模
- 统计推理 统计推理
- 动态系统 动态系统
背景情况:
- 延迟微分方程 (DDE) 对于模拟具有时间延迟的系统至关重要.
- 之前的DDE推理方法经常对模型结构施加限制性假设.
- 具有多个延迟的多变量DDEs带来了重大的推断挑战.
研究的目的:
- 为多变量延迟微分方程模型开发一个灵活的最大概率推理框架.
- 克服以前方法的局限性,不要假设DDEs的特定形式.
- 为了使最大概率推理能够应用于更广泛的DDE模型类别.
主要方法:
- 开发一个最大概率推断框架,用于一般的DDEs.
- 实现自适应网格和梯度下降数值算法.
- 制定估计信息矩阵和构建置信区间的方法.
主要成果:
- 建立了一个强大的框架,用于在具有一个或多个延迟参数的多变量DDE中推断最大概率.
- 数字算法 (自适应网格,梯度下降) 已开发用于参数估计和信息矩阵计算.
- 该框架在流行病和药理动力学模型上进行了演示,显示了其实际适用性.
结论:
- 开发的框架为分析DDE模拟的复杂系统提供了一个强大的工具.
- 该方法的通用性允许在科学研究中更广泛地应用,包括流行病学和药理学.
- 这项工作为一类重要的动态系统推进了统计推理能力.
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