新的贝叶斯推理方法使用神经估计器处理复杂模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新的半摊销贝叶斯推理方法，将高维模型分解为更小的块。该方法使用神经估计器来近似计算成本高昂或频繁更新的块的条件后验。然后，马尔可夫链在直接从这些估计器抽取样本和对其他变量进行常规更新之间交替进行，从而无需似然评估或接受-拒绝步骤即可实现高效推理。该方法可以在不同块和数据集之间重用估计器，使其适用于复杂的层次模型和时空模型。 AI

影响该方法可以提高贝叶斯推理在复杂机器学习模型中的效率和适用性。

排序理由该集群包含一篇详细介绍贝叶斯推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Niek Stevenson, Paul-Christian B\"urkner, Andrew Heathcote, Stefan T. Radev · 2026-10-15 04:00

半摊销贝叶斯推理与可复用局部后验

arXiv:2610.18945v1 Announce Type: cross Abstract: Amortized Bayesian inference uses simulations to perform Bayesian estimation when likelihoods are expensive or analytically unavailable. However, learning the full joint posterior is challenging in high-dimensional models with com…

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半摊销贝叶斯推理与可复用局部后验

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