用消息传递实现多变量自回归模型的在线贝叶斯估计
Online Bayesian system identification in multivariate autoregressive models via message passing
- 基于因子图的消息传递，递归更新参数后验分布
- 能输出系数和噪声精度的完整后验，支持预测不确定性量化
- 适合需要在线更新与可信度评估的系统辨识任务
我们提出一种基于因子图中消息传递的递归贝叶斯估计方法，用于带外生输入的多变量自回归模型。与递归最小二乘法不同，该方法可获得自回归系数和噪声精度的完整后验分布。这些估计的不确定性会传播至未来系统输出的预测不确定性，并支持在线模型证据计算。我们在一个合成自回归系统上验证了收敛性，在双质量-弹簧-阻尼系统上展示了具有竞争力的表现。
原文摘要 · Abstract (English)
We propose a recursive Bayesian estimation procedure for multivariate autoregressive models with exogenous inputs based on message passing in a factor graph. Unlike recursive least-squares, our method produces full posterior distributions for both the autoregressive coefficients and noise precision. The uncertainties regarding these estimates propagate into the uncertainties on predictions for future system outputs, and support online model evidence calculations. We demonstrate convergence empirically on a synthetic autoregressive system and competitive performance on a double mass-spring-damper system.
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