Linear parameter varying (LPV) method is an effective tool for nonlinear process modeling

which converts modeling of multi-stage nonlinear complex process into identification of multiple linear models. Various disturbance factors in industrial processes result in stochasticity of system modeling and uncertainty of model parameters. Identification of LPV models was studied under the variational Bayesian (VB) framework. After prior probability distributions were assigned to variables and parameters

posterior distributions of these variables and parameters were estimated by maximizing lower limits of objective functions. This full Bayesian system identification approach not only provided point estimates of parameters

but also quantified uncertainty of estimation. Numerical simulation on typical two-stage process and continuous stirred tank reactor (CSTR) demonstrated effectiveness and superiority of the proposed method.