基于预测状态表示的多变量概率系统预测

针对由于多变量概率系统预测高复杂度而导致的建模困难问题，提出了一种基于预测状态表示（PSR）的系统建模新方法，首先介绍一种通用多变量过程概念，并进一步用此概念描述多变量系统。在此基础上，引入了针对多变量系统的预测模型MV PSR，模型基于可观测信息，可在有限维实现对多变量的预测。实验结果表明，该近似模型有效降低了系统预测的复杂度。

Prediction of multivariate probabilistic systems based on predictive state representation

In this paper, a new method based on Predictive State Representation (PSR) was proposed to solve high complexity of multivariate probability system. The paper introduced a new concept about general multivariate process in the first, and then described the multivariate system with the concept. Furthermore, the authors imported MultiVariate (MV) PSR as prediction model for multivariate system. The model was based on observable information and could realize the multivariate prediction in the finite dimensions. The experimental result shows that the approximate model effectively reduces the complexity of the system prediction.