School of Electronics and Computer Science, University of Southampton, Southampton, UK.
Neural Comput. 2010 Aug;22(8):1993-2001. doi: 10.1162/neco.2010.07-09-1047.
Physiological signals such as neural spikes and heartbeats are discrete events in time, driven by continuous underlying systems. A recently introduced data-driven model to analyze such a system is a state-space model with point process observations, parameters of which and the underlying state sequence are simultaneously identified in a maximum likelihood setting using the expectation-maximization (EM) algorithm. In this note, we observe some simple convergence properties of such a setting, previously un-noticed. Simulations show that the likelihood is unimodal in the unknown parameters, and hence the EM iterations are always able to find the globally optimal solution.
生理信号，如神经尖峰和心跳，是时间上的离散事件，由连续的基础系统驱动。最近引入的一种用于分析此类系统的数据驱动模型是具有点过程观测的状态空间模型，其参数和基础状态序列使用期望最大化 (EM) 算法在最大似然设置中同时识别。在本说明中，我们观察到了这种设置的一些以前未注意到的简单收敛性质。模拟表明，似然函数在未知参数中是单峰的，因此 EM 迭代总能找到全局最优解。
