研究人员开发了新颖的马尔可夫链蒙特卡洛（MCMC）采样方法，专注于提高效率和鲁棒性。一种方法引入了基于核差异的内在有效样本量度量，旨在对流形值样本的坐标系变化保持不变。另一种方法APM-SGHMC使用自适应主成分变换来创建用于贝叶斯结构系统识别的旋转不变采样器，在不重新训练的情况下展示了跨不同任务的零样本泛化能力。 AI
影响 MCMC采样的这些进展可以提高复杂贝叶斯推理任务的效率和可靠性，可能影响依赖概率建模的领域。
排序理由 该集群包含两篇arXiv论文，详细介绍了MCMC采样的新方法。
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arXiv:2605.03266v1 Announce Type: cross Abstract: Effective sample size is a standard summary of Markov chain Monte Carlo output, but it is usually attached to scalar or Euclidean summaries chosen by the analyst. For manifold-valued samples this choice is not canonical: coordinat…
Effective sample size is a standard summary of Markov chain Monte Carlo output, but it is usually attached to scalar or Euclidean summaries chosen by the analyst. For manifold-valued samples this choice is not canonical: coordinate-wise effective sample sizes can change under rot…
arXiv:2604.23381v1 Announce Type: cross Abstract: Over decades, Markov chain Monte Carlo (MCMC) methods have been widely studied, with a typical application being the quantification of posterior uncertainties in Bayesian system identification of structural dynamic models. To addr…
Over decades, Markov chain Monte Carlo (MCMC) methods have been widely studied, with a typical application being the quantification of posterior uncertainties in Bayesian system identification of structural dynamic models. To address the issue of excessively low sampling efficien…