Abstract We introduce stochastic asymptotical regularization (SAR) methods for the uncertainty quantification of stable approximate solution ill-posed linear-operator equations, which are deterministic models numerous inverse problems in science and engineering. demonstrate that SAR can quantify error estimates problems. prove regularizing properties with regard to mean-square convergence. also...