论文标题
类似于Pac-Bayesian的误差绑定了一类线性时间存在随机状态空间模型
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
论文作者
论文摘要
在本文中，我们得出了一类带有输入的随机动力系统的类似pac-bayesian的误差，即，对于线性时间不变的随机随机态空间模型（短时间的LTI系统短）。这类系统广泛用于控制工程和计量经济学，它们代表了经常性神经网络的特殊情况。在本文中，我们1）正式化了带有输入的随机LTI系统的学习问题，2）得出此类系统绑定的类似pac-bayesian的错误，3）讨论此错误绑定的各种后果。
In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.
