论文标题
分布式贝叶斯学习动态状态
Distributed Bayesian Learning of Dynamic States
论文作者
论文摘要
这项工作研究了在部分信息下跟踪自然动态状态的网络代理。所提出的算法是用于有限状态隐藏马尔可夫模型（HMM）的分布式贝叶斯过滤算法。它可用于顺序状态估计任务，以及在动态环境下对社交网络的意见形成进行建模。我们表明，与最佳集中式解决方案的分歧是在几何偏僻的状态过渡模型中渐近界定的，其中包括快速变化的模型。我们还得出了用于计算误差概率并在高斯观察模型下建立收敛的递归。提供了模拟来说明理论并与其他方法进行比较。
This work studies networked agents cooperating to track a dynamical state of nature under partial information. The proposed algorithm is a distributed Bayesian filtering algorithm for finite-state hidden Markov models (HMMs). It can be used for sequential state estimation tasks, as well as for modeling opinion formation over social networks under dynamic environments. We show that the disagreement with the optimal centralized solution is asymptotically bounded for the class of geometrically ergodic state transition models, which includes rapidly changing models. We also derive recursions for calculating the probability of error and establish convergence under Gaussian observation models. Simulations are provided to illustrate the theory and to compare against alternative approaches.
