Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference
基于贝叶斯推断的动力系统识别、模型选择与不确定性量化
This study presents a Bayesian maximum \textit{a~posteriori} (MAP) framework for dynamical system identification from time-series data. This is shown to be equivalent to a generalized Tikhonov regularization, providing a rational justification for the choice of the residual and regularization terms, respectively, from the negative logarithms of the likelihood and prior distributions. In addition to the estimation of model coefficients, the Bayesian interpretation gives…