离散时间序列的序贯估计与预测
On Sequential Estimation and Prediction for Discrete Time Series
摘要：
如何从平稳随机过程$X_0,X_1,...X_n$的观测序列中提取尽可能多的信息，这一问题已被许多作者从不同的角度考虑过。这是通过D. Bailey指出，对于$\textbf{P}(X_{n+1}|X_0,X_1,...X_n)$，没有一个普适的估计量能几乎肯定地收敛于真实的估计量。尽管这样的结果，对于有限的类的过程，或序列的估计沿停时，通用估计可以找到。我们在这里提出了一个调查的一些最近的工作，已经沿着这些路线。
英文摘要：
The problem of extracting as much information as possible from a sequence of observations of a stationary stochastic process $X_0,X_1,...X_n$ has been considered by many authors from different points of view. It has long been known through the work of D. Bailey that no universal estimator for $\textbf{P}(X_{n+1}|X_0,X_1,...X_n)$ can be found which converges to the true estimator almost surely. Despite this result, for restricted classes of processes, or for sequences of estimators along stopping times, universal estimators can be found. We present here a survey of some of the recent work that has been done along these lines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
