Bayesian inference for ordinary differential equations models with heteroscedastic measurement error
异方差测量误差下常微分方程模型的贝叶斯推断
Ordinary differential equation (ODE) models are widely used to describe systems in many areas of science. To ensure these models provide accurate and interpretable representations of real-world dynamics, it is often necessary to infer parameters from data, which involves specifying the form of the ODE system as well as a statistical model describing the observational process. A popular and convenient choice for the error model is a Gaussian distribution with constant…