Abstract We consider Bayesian inference for large-scale inverse problems, where computational challenges arise from the need repeated evaluations of an expensive forward model. This renders most Markov chain Monte Carlo approaches infeasible, since they typically require <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"...

We consider an unconstrained problem of minimizing a smooth convex function which is only available through noisy observations its values, the noise consisting two parts. Similar to stochastic optimization problems, first part nature. The second additive unknown nature but bounded in absolute value. In two-point feedback setting, i.e., when pairs values are available, we propose accelerated der...