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English(EN) Folded Transport MCMC: Certifiable Quotient Posterior Computation for Symmetric Bayesian Models

新的MCMC方法解决了具有对称性的贝叶斯模型

研究人员开发了一种名为Folded Transport MCMC (FolT-MCMC) 的新MCMC方法,以应对具有对称性的贝叶斯模型中的挑战。该方法通过使用学习到的归一化流在对称群的基本域上构建独立采样器,直接推断商后验。FolT-MCMC在收敛诊断和认证下界方面提供了显著改进,在各种混合模型和真实世界数据上实现了2倍至145倍的提升。 AI

影响 为贝叶斯推理引入了一种新颖的计算技术,有可能提高AI研究中使用的模型的效率和可靠性。

排序理由 该集群包含一篇详细介绍新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的MCMC方法解决了具有对称性的贝叶斯模型

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该集群包含一篇详细介绍新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jun Hu ·

    折叠式马尔可夫链蒙特卡洛:对称贝叶斯模型的认证商后验计算

    arXiv:2606.04307v1 Announce Type: new Abstract: Bayesian models with finite symmetry - mixture models with exchangeable components, structural identification with closely-spaced modes - define posteriors that are invariant under a group of label permutations, creating redundant m…