Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling
贝叶斯惩罚经验似然与马尔可夫链蒙特卡洛抽样
Abstract In this study, we introduce a novel methodological framework called Bayesian penalized empirical likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to facilitate the use of well-established Markov Chain Monte Carlo sampling schemes as a convenient alternative to…