English(EN) Automatic Differentiation for Inference on Partially Observed Markov Process Models

新框架增强了复杂动态系统的自动微分能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一个新的理论框架，用于部分观测马尔可夫过程模型的推理自动微分（AD）。该框架将现有的AD粒子滤波方法嵌入到一类新算法中，实现了偏差-方差权衡，与先前的方法相比，显著降低了均方误差。新算法只需要一个可微分的潜在动态系统模拟器，而大多数先前的方法需要访问系统的转移概率。数值结果表明，在具有挑战性的科学基准问题上取得了实质性改进，特别是在使用混合方法（该方法改进了迭代滤波算法的粗略解）时。 AI

影响增强了复杂动态系统的推理能力，可能改进依赖于深度学习和高级统计建模的领域的应用。

排序理由该集群包含一篇学术论文，详细介绍了针对特定统计建模问题的新理论框架和算法。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv stat.ML TIER_1 English(EN) · Kevin Tan, Aaron J. Abkemeier, Kunyang He, Giles Hooker, Edward L. Ionides · 2026-10-23 04:00

Automatic Differentiation for Inference on Partially Observed Markov Process Models

arXiv:2407.03085v2 Announce Type: replace-cross Abstract: Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed nonlinear stochastic dynamical systems have…

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Automatic Differentiation for Inference on Partially Observed Markov Process Models

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