A fully Bayesian approach to kernel-based regularization for impulse response estimation
A fully Bayesian approach to kernel-based regularization for impulse response estimation is a scholarly work, published in 2018 in ''IFAC Proceedings Volumes''. The main subjects of the publication include regularization, hyperparameter, impulse response, artificial intelligence, computer science, system identification, fault detection and isolation, applied mathematics, mathematics, pattern recognition, Gaussian process, algorithm, and Bayesian probability. The authors develop an alternative way to obtain kernel-based regularization estimates by Bayesian model mixing.