Minimization of a stochastic cost function is commonly used for approximate sampling in high-dimensional Bayesian inverse problems with Gaussian prior distributions and multimodal posterior distributions. The density the samples generated by minimization not desired target density, unless observation operator linear, but distribution useful as proposal importance or Markov chain Monte Carlo met...