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English(EN) A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Quantile Regression and Bayesian Model Selection

新的两步Metropolis-Hastings算法增强了贝叶斯经验似然方法

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的两步Metropolis-Hastings算法，旨在提高贝叶斯经验似然（BayesEL）方法马尔可夫链蒙特卡洛（MCMC）采样的效率。这种新方法解决了先前限制BayesEL应用的复杂性和非凸性问题，尤其是在联合分位数回归等领域。该算法通过使用当前参数值来提议剩余参数的新值，从而促进了从BayesEL后验分布的采样，并且可以通过可逆跳跃MCMC程序扩展到贝叶斯模型选择。 AI

影响 这项研究引入了一种更有效的贝叶斯统计推断计算方法，通过提高复杂模型分析的准确性和可行性，可能间接惠及AI研究。

排序理由 该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

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新的两步Metropolis-Hastings算法增强了贝叶斯经验似然方法

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该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sanjay Chaudhuri, Teng Yin, Snehashis Chakraborty, Rupsa Roy ·

    用于贝叶斯经验似然计算的二步Metropolis Hastings方法及其在分位数回归和贝叶斯模型选择中的应用

    arXiv:2209.01269v2 Announce Type: replace-cross Abstract: Empirical likelihood-based methods have been used under the Bayesian framework (BayesEL) in recent times. For statistical inference, these methods require efficient Markov chain Monte Carlo (MCMC) samplers for drawing obse…

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