Sinkhorn Hamiltonian Monte Carlo for Entropic Optimal Transport Generalized Bayes
Sinkhorn 哈密顿蒙特卡洛:用于熵正则最优传输广义贝叶斯
Bayesian posterior sampling is a ubiquitous paradigm for problems where a point estimate of parameters is not sufficient, such as risk analysis and uncertainty quantification. However, likelihoods may be misspecified, intractable, computationally expensive, or not representative of the discrepancy of interest. Generalized Bayes extends likelihood-based posterior updates by using other losses. Sinkhorn divergences have appealing geometric properties: they compare empirical…