更新时间概率模型及其无偏估计定理的论证

本文提出应用小参数法 ,探讨 Markov链中相邻两次更新时刻内稀疏事件的概率估计问题 .建立了三种最重要的具有更新时间的概率模型 .通过小参数的引入和对概率式的幂展开 ,进而推证出幂渐近展开系数的模型估算法 .论证了无偏估计的重要定理 ,给出了概率估计式和无偏估计精度 .亦将许多算法扩展到Markov链的任意状态空间

The Proof for Probable Models with Renewable Time and the Calculating Theorems of Without Deviation

In this paper presented utilization of the method of small parameters, in order to that inquire into probable estimate the question of sparse ineident in near neighbor two renewal times in Markov Chain. And built three types better vital probable models with renewable time. And through the introduction of the small parameters and the unfold of power for the probable formulas, and then a model estimation-mothod of gradual spread coefficient with form of power is proved. And also proved important theorems about estimate of without deviation. And the probable calculated formulas and the estimate precision of without deviation are given. And some algorithms extend to the arbirary space of a state.