题    目: Early rejection methods for inferring ordinary differential equation models using distance prediction

报告人: 田天海教授

单    位: Monash University, Australia

时    间: 2025年4月1日16:00

地    点:数学与统计学院二楼会议室

摘    要: Ordinary differential equations (ODEs) are powerful tools for modeling a wide range of biological systems. To reduce the computing time in the inference of large-scale ODE models, we propose early rejection algorithms that utilize a distance prediction function to enhance the efficiency of Approximate Bayesian Computation (ABC) algorithms. This distance prediction function approximates the distance between the generated simulation and experimental data for each parameter sample. We apply this approach to a gene network model to examine the impact of functions on the method’s effectiveness and efficiency. Numerical results from two cell signalling pathways demonstrate that the proposed early rejection methods significantly enhance the efficiency of ABC algorithms.

简    介: 田天海：2001年在澳大利亚昆士兰大学取得博士学位；研究领域包括基因网络和细胞信号传导等生物系统的随机建模、随机动力系统的数值模拟、模型参数估计和统计分析与计算；获得澳大利亚研究基金会的“未来研究员”（Future Fellow）和“澳大利亚研究员”（Australian Fellow）以及格拉斯哥大学“凯尔文爵士研究员”（Lord Kelvin Fellow）等称号；研究成果已发表在多个高级别的学术刊物上，包括“自然杂志细胞生物学分刊”、“美国国家科学院会刊”和“当代生物学”等杂志。