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posterior pereion相关的网络例句

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与 posterior pereion 相关的网络例句 [注:此内容来源于网络,仅供参考]

In the normal rats (n=5、6), CGRP-LI were mainly present in small and some medium-sized neurons and the primary afferent fibers and ter- minals in laminae Ⅰ,Ⅱ,Ⅴ,Ⅹ and Lissauer's tract of the spinal cord; GAL- and SP-LI were mainly present in small neurons and the superficial layers of the dorsal horn. After the adjuvant injection both CGRP-and SP-LI were in- creased corresponding to the development of arthritis. On day 2, CGRP-and SP-LI were moderately increased in laminae Ⅰ,Ⅱ and the posterior commis- sure of the spinal cord and small neurons in DRG, while SP was obviously in- creased in the superficial layers and DRG. On day 14, CGRP-LI were marked- ly enhanced in the above regions with the development of polyarthritis. Some- times many strongly stained long fibers could be seen in the posteriot commis- sure.

在DRG中,CGRP主要分布于小和中等神经元之中,SP和GAL主要分布于小神经元之中;2、注入弗氏完全佐剂(含冻干结核菌0.5mg)后第2天动物(各5~6例)产生具有明显红、肿、热、痛和痛敏的急性关节炎,免疫组化实验观察到注射侧脊髓背角和DRG中CGRP-和SP-LI明显增加;在佐剂后第14天,动物患肢肿胀、痛敏和自发痛均进一步加重,部分动物健肢也出现红肿,产生多发性关节炎时,脊髓背角浅层和DRG中CGRP-和SP-LI增加更为明显,并且在灰质后联合中可见许多深染的CGRP-LI长纤维;在佐剂后第21和28天,随着佐剂性关节炎的逐渐恢复,脊髓背角和DRG中CGRP-和SP-LI也接近正常。

Another way to approximate the posterior state distribution is Monte Carlo simulation.

对目标后验状态分布近似的另一条途径是Monte Carlo仿真。

The general process for Bayes analysis is: prior distribution + data = posterior distribution.

Bayes分析的一般程序是先验分布+数据=后验分布。

Using the posterior distribution, we analyzed the influence of a particular datum as well as the data of one individual, on the estimates of the parameters.

我们在第三章采用分层模型思想,导出了效应参数的后验分布,并以这一分布为基础讨论了混合效应模型的Bayes局部影响分析。

From the twenty century, the computer technology has great development, which made the computation of the posterior distribution based on the multidimensional integral possible.

二十世纪以来,计算机技术得到了空前的发展,这使得以多维积分为基础的贝叶斯后验概率的计算成为可能,这也推动了贝叶斯计量经济学的发展。

Within methods of this class, tracking is solved by sequential building posterior probability distribution of target state, and sampling based Monte Carlo computing acts as one of the key technologies.

这类方法通过序贯构造目标状态的后验概率分布来求解跟踪问题,而基于抽样的蒙特卡罗计算方法则是其中关键的统计计算技术。

Bayesian method is adopted to determine the posterior distribution by using the prior information, population information and sample information.

结合当前样本信息,利用贝叶斯方法得到捷联惯组历次测试数据的验后分布。

When some parameters undergo the nonlinearity model, this algorithm can exactly present the posterior distribution, which is able to make full use of sensor measurement message every time, and optimize measurement variance.

按实际的非线性模型演化时,该算法能够很好地对非线性函数的后验均值与方差进行拟合,并充分利用了传感器每次量测带来的信息并进一步优化了测量方差。

The difficulty in statistical inference using Bayesian approach is calculating the posterior margin distribution, in most situations, it is very difficult to do so and even the formula can not be got.

运用Bayes 统计分析的方法进行统计推断的难点在于其后验边际分布的计算,在很多情况下很难,甚至不可能得到后验边际分布的解析表示,有时虽然可能得到解析表示但是结果相当复杂,不便于应用和推广。

Front stress distribution of plate was all higher than posterior. The stress distribution of plate Ⅱ was not most uniform.

背伸20°位3种固定方式的钢板应力分布都不均匀,钢板前部应力水平均高于后部。

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