Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-differentiable Priors
非可微先验下贝叶斯推断的梯度型 MCMC
The use of nondifferentiable priors in Bayesian statistics has become increasingly popular, in particular in Bayesian imaging analysis. Current state-of-the-art methods are approximate in the sense that they replace the posterior with a smooth approximation via Moreau-Yosida envelopes, and apply gradient-based discretized diffusions to sample from the resulting distribution. We characterize the error of the Moreau-Yosida approximation and propose a novel implementation using…