The equation of sequential sampling is proposed.
并根据研究结果提出了序贯抽样方程。
Otherwise, the optimal theoretical sampling number of the larva and the sequential sampling model were obtained.
并确定了该天牛幼虫林间调查的最适理论抽样数和序贯抽样模型。
By introducing the idea of sequential sampling, a method for testing correlation coefficients of meteorological variables is presented.
本文引进序贯抽样的思想，提出气象变量相关系数的一种序贯检验方法。
The model data conversion, the population sequential sampling and the theoretical sampling number model were also built up in the paper.
在分布型研究基础上，探讨了资料代换模型，最适抽样数和种群序贯抽样模型。
Based on this, Ma Zhan-shan's Model (1988) is applied to the Simple Sequential Sampling Analysis, and Making for its Analysis table and figure.
在此基础上，采用马占山(1988)的简易序贯抽样决策模型进行了简易序贯抽样分析，制作了简易序贯抽样分析图、表。
The optimum sampling number, the sequential sampling application and the sampling method at different densities were determined based on the characteristics of the larva's distribution.
根据竹螟幼虫分布的特点，确定了不同密度下的最适抽样数、序贯抽样的应用和调查取样方法。
In this paper, a new particle filter based on sequential importance sampling (SIS) is proposed for the on-line estimation problem of non-Gauss nonlinear systems.
针对非线性、非高斯系统状态的在线估计问题，本文提出一种新的基于序贯重要性抽样的粒子滤波算法。
In particle filters (PF), sequential importance sampling will result in sample impoverishment and further the loss of diversity after resampling.
粒子滤波算法(PF)中，序列重要性采样引起采样点贫化，进一步经过重采样后造成分集度损失。
The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.
再采样粒子滤波虽可缓解粒子退化，但易导致样本贫化；扩展粒子滤波也可在一定程度上解决退化问题，但难以跟踪突变状态。
This paper proposed a new sampling plan, the sequential mesh test, in order to overcome the disadvantages of the widely used Sequential Probability Ratio Test (SPRT).
针对序贯概率比检验（SPRT）无法控制抽取样本量等不足之处，提出了一种改进的抽样检验方法——序贯网图检验。
A single Gaussian distribution is obtained to approximate the posterior distribution of state parameters based on sequential importance sampling and Monte Carlo methods.
通过基于重要性采样和蒙特卡罗模拟方法得到一高斯分布来近似未知状态变量的后验分布。
A single Gaussian distribution is obtained to approximate the posterior distribution of state parameters based on sequential importance sampling and Monte Carlo methods.
通过基于重要性采样和蒙特卡罗模拟方法得到一高斯分布来近似未知状态变量的后验分布。
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