English(EN) Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation

新的马尔可夫链蒙特卡洛采样器增强了切换随机微分方程的贝叶斯推断

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了一种用于切换随机微分方程（SSDEs）的新型近似马尔可夫链蒙特卡洛（MCMC）采样器。该方法利用均匀化和时间条件因子化神经网络似然估计（FNLE）来解决SSDEs的贝叶斯推断中的挑战。所提出的采样器具有广泛的适用性，克服了先前方法的局限性，例如需要无噪声观测或单变量状态，并在合成实验中成功恢复了状态路径和参数，并在真实世界数据中检测到了状态转换。 AI

影响这种用于SSDEs推断的新方法可以实现对从生物学到金融学等领域中复杂动态系统的更准确建模。

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 →

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arXiv cs.LG TIER_1 English(EN) · Shion Hosoda, Michiaki Hamada · 2026-10-08 04:00

基于均匀化和时间条件因子化神经似然估计的用于切换随机微分方程的广泛适用的近似MCMC

arXiv:2610.10194v1 Announce Type: cross Abstract: Switching stochastic differential equations (SSDEs) describe continuous-time dynamics whose parameters switch according to a latent regime process that follows a continuous-time Markov chain (CTMC). By allowing dynamics to change …

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基于均匀化和时间条件因子化神经似然估计的用于切换随机微分方程的广泛适用的近似MCMC

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