对大气强抖动和弱抖动模式下空间光信道衰减模型做了深入分析，并用似然检测分析了系统性能。
The model of the optical channel in strong and weak turbulences is analyzed, the system performance is studied by maximum probability detection.
本文构造了非线性模型中参数的经验欧氏似然比统计量，并证明了该似然估计的强相合性和渐近正态性。
In this paper, empirical Euclidean likelihood ratio statistics are constructed for parametric in a nonlinear model. And prove strong consistency and asymptotic normality of the estimation.
文中我们讨论了更新过程中分布参数的最大似然估计，证明了最大似然估计是强相合的。
The Maximum Likelihood Estimator (MLE) of the parameter of interarrival distribution based on renewal process is discussed. It is obtained that the MLE converges strongly to the true parameter.
证明估计的强相合性和渐近正态性，给出似然比检验统计量的极限分布，并讨论基于精确分布的检验问题。
The limit distributions of estimators and likelihood ratio test are given, the strong consistency of estimators is also proved.
第四章讨论了序贯指数模型的极大似然估计的强相合性和渐近正态性，并进行了证明。
In Chapter 4, we discuss and prove the consistency and asymptotic normality of maximum likelihood estimate to the exponential models.
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and m…
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and maximum likelihood …
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and maximum likelihood …
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