Probabilistic-Numeric SMC Sampling for Bayesian Nonlinear System Identification in Continuous Time

arXiv:2404.12923v1 Announce Type: cross
Abstract: In engineering, accurately modeling nonlinear dynamic systems from data contaminated by noise is both essential and complex. Established Sequential Monte Carlo (SMC) methods, used for the Bayesian identification of these systems, facilitate the quantification of uncertainty in the parameter identification process. A significant challenge in this context is the numerical integration of continuous-time ordinary differential equations (ODEs), crucial for aligning theoretical models with discretely sampled data. This integration introduces additional numerical uncertainty, a factor that …

abstract arxiv bayesian challenge continuous cs.lg data dynamic engineering identification modeling noise process quantification sampling stat.ml systems type uncertainty