Sampling-free linear Bayesian update of polynomial chaos representations

► We cast the probabilistic identification problem in a linear Bayesian setting based on a functional approximation. ► The update procedure does not involve sampling at any stage of the computation. ► The method can handle non-Gaussian random variables. ► It is applicable to nonlinear systems. ► The method is compared with the Ensemble Kalman Filter (EnKF).