ONLINE VARIATIONAL MESSAGE PASSING IN THE HIERARCHICAL GAUSSIAN FILTER
ONLINE VARIATIONAL MESSAGE PASSING IN THE HIERARCHICAL GAUSSIAN FILTER
复制标题
分级高斯滤波器中的在线变分消息传递
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
B. Vries
中科院分区：
文献类型：
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作者：
Ismail Senöz;B. Vries
研究了层次贝叶斯非线性动态系统的在线状态和参数估计问题。我们关注的是在计算神经科学文献中流行的一种模型——层次高斯滤波器（HGF）。对于这种滤波器，之前已经导出了在线状态估计（和离线参数估计）的显式方程。我们扩展了这项工作，将HGF转换为一个概率因子图，并提出了变分消息传递更新规则，该规则既方便在线状态和参数估计，也方便在线跟踪自由能（或ELBO），它可以用作贝叶斯证据的代理。由于因子图框架的局部性和模块化，我们的方法支持将HGF及其变体作为插件模块应用于各种动态建模应用程序。
We address the problem of online state and parameter estimation in hierarchical Bayesian nonlinear dynamic systems. We focus on the Hierarchical Gaussian Filter (HGF), which is a popular model in the computational neuroscience literature. For this filter, explicit equations for online state estimation (and offline parameter estimation) have been derived before. We extend this work by casting the HGF as a probabilistic factor graph and present variational message passing update rules that facilitate both online state and parameter estimation as well as online tracking of the free energy (or ELBO), which can be used as a proxy for Bayesian evidence. Due to the locality and modularity of the factor graph framework, our approach supports application of HGF’s and variations as plug-in modules to a wide variety of dynamic modelling applications.