Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood Computation
贝叶斯经验似然计算中的哈密顿蒙特卡洛抽样
Summary We consider Bayesian empirical likelihood estimation and develop an efficient Hamiltonian Monte Carlo method for sampling from the posterior distribution of the parameters of interest. The method proposed uses hitherto unknown properties of the gradient of the underlying log-empirical-likelihood function. We use results from convex analysis to show that these properties hold under minimal assumptions on the parameter space, prior density and the functions used in the…