Abstract This work unifies the analysis of various randomized methods for solving linear and nonlinear inverse problems with Gaussian priors by framing problem in a stochastic optimization setting. By doing so, we show that many are variants sample average approximation (SAA). More importantly, able to prove single theoretical result guarantees asymptotic convergence variety methods. Additional...