PosteriorRefinementImprovesSampleEfficiency inBayesianNeuralNetworks

Due to the non-linearity of NNs, no analytic solution to the integral exists, even when the likelihood and the approximate posterior are both Gaussian. A low-cost, unbiased, stochastic approximation can be obtained via Monte Carlo (MC) integration: obtainS samples from the approximate posterior and then compute the empirical expectation of the likelihood w.r.t.