后验分布

In this algorithm, the direction-of-arrival is assumed to be a discrete random variable with a priori probability density function defined on a set of candidate points. Whether or not the secondary sample is required is based on the a posteriori probability distribution of a set of candidate point"s, which can be calculated from the array received signals. And then, the resulting beamformer is a weighted sum of the beamformers pointed at the latest set of point"s, which are combined according to the value of the a posteriori probability for each pointing direction.

在该算法中，期望信号的波达方向被看作是一个定义在若干候选波达方向上的离散随机变量，由阵列接收信号计算出各候选波达方向的后验概率，并将后验概率的分布作为是否需要进行二次采样的判断标准；随后，对各波达方向上的波束成形权值线性加权，便可得到自适应波束成形器，权系数为各候选波达方向的后验概率值。

The case study shows that the method proposed can provide the posterior distribution of parameters of Nash model based on posterior information of parameters.

实例研究表明，该方法能充分利用已知的后验信息获取Nash模型参数的不确定性，得到其后验分布。

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。

Further more, based on the analysis of Bayesian AR model and MA model , we analyzed the Bayesian ARMA model and its mathematical structure. Mainly analyzed the Bayesian ARMA(1,1) model, and constructed the model condition likelihood function and the parameters posterior distribution.

同时，以时间序列AR模型及MA模型的贝叶斯分析为基础进行了时间序列ARMA模型的贝叶斯分析，从分析ARMA模型的数学结构开始，重点进行了ARMA(1，1)模型的贝叶斯分析；构建了模型的条件似然函数和参数的先验分布，推导其参数的条件后验密度和边缘后验密度；并借用一组SAS软件模拟的ARMA(1，1)序列，通过WinBUGS进行仿真分析。

Bayesian belief propagation algorithm was used to efficiently calculate the maximum of the posterior probability of background with input data.

在建立正确模型参数的基础上，贝叶斯信念传播算法根据图像输入预测当前背景状态的后验分布，并根据预测得到的背景状态对输入图像进行分割。

Its aim is to improve the generalization ability of networks, and it trains a network with the purpose of obtaining the weights corresponding with the maximum posterior probability.

该算法是一种新型前馈神经网络训练算法，它以提高网络的泛化性能为主旨，其训练目标在于获取对应于后验分布最大值的权值向量。

First,we proposes the acceleration of Monte Carlo EM Algorithm,which is based on Monte Carlo EM Algorithm and Newton-Raphson algorithm,to improve the convergence rate;Second,the it is shown that the accelerated EM algorithm we proposed has quadratic convergence rate in a neighborhood of the posterior mode;Finally,its excellent performance in convergence rate is illustrated by a classical example.

受Monte Carlo EM算法与EM加速算法启发，本文构造了一种新的EM算法，称为Monte Carlo EM加速算法；证明了该算法在似然函数/后验分布的众数的附近确实具有二次收敛速度，改进了Monte Carlo EM算法的收敛速度；并通过一个数值例子的计算结果说明了该算法的优良性，它兼具实现简单及收敛速度快的特点。

Secondly, the properties of output sequences of stop-and-go clocked combiners are discussed. It is proved that the output sequences are stationary and obey strong law of large numbers and central limit theorem. Using the probabilistic model properly built, we analyze the rate of coincidence between the output sequences of two kinds of generators with basic operations and corresponding LFSRs sequences. All the computational formulae are derived. A recursive algorithm for efficiently computing the poster probability of partial input strings and corresponding joint probability is introduced. Moreover, divide-and-conquer attacks on this kind of generators which reconstruct the initial states of the input stop-and-go generators individually are proposed.

其次，分析了一般钟控停走组合生成器输出序列的概率分布性质，证明了生成器的输出序列是严平稳的，且服从强大数定律和中心极限定理；考察了钟控加法型组合生成器和钟控乘法型组合生成器的输出序列和相应的常规钟控下线性移位寄存器序列之间的符合率，给出了符合率的具体计算公式；给出了组合器输出序列段和输入序列段之间的联合分布以及部分输入序列段的后验概率的有效递归算法，进一步提出了利用后验概率对钟控组合生成器进行分别征服相关攻击的方法。

Topics covered include subjective notion of probability, Bayes' Theorem, prior and posterior distributions, and data analysis techniques for statistical models.

包括：概率的主观定义，贝叶斯理论，先验和后验分布，统计模型的数据分析技术。

In view of the peaked and fat-tailed characteristics of financial return data distribution and its effect of clustering fluctuation and especially the "leverage effect" of fluctuation on VaR estimates and some efficiencies when estimating VaR with various assumptions of return data distribution,a semi-parameter approach based on EGARCH-VaR model is developed.

在综合考虑了金融收益数据分布的尖峰厚尾特征及其波动集群性，尤其是其波动的&杠杆效应&对VaR估计的影响以及各种假定收益率分布在计算风险价值时存在不足的基础上，提出了基于EGARCH-VaR的半参数方法，并且与正态分布和t分布假设下的GARCH模型的VaR计量方法进行比较，通过实证分析，并利用后验测试，表明基于EGARCH-VaR的半参数方法对风险价值的测度优于正态分布和t分布假设下GARCH模型的VaR计量方法。

Cynanchum Lingtai apricot production in the average weight 65 grams, the brightly-colored fruit, juicy rich, sweet-sour taste, sweet from the nucleolus, when the late Qing Dynasty famous Shaanxi, Gansu provinces, the Qing imperial court Tongzhi tribute for years.

灵台生产的牛心杏平均单果重65克，果实色泽鲜艳，汁多味浓，甜酸适口，离核仁甜，清末时就驰名陕、甘两省，清同治年间曾为朝廷贡品。

Chenopodium album,Solanum nigrum, and Amaranthus retroflexus were very susceptible to the herbicides. Polygonum persicaria and Abutilon theophrasti were relatively less susceptible to the herbicides, and Lycopersicon esculentum was not susceptible to it. The relationship between reduction rates of weed biomass and PPM values of weed leaves 2,4, and 6 days after treatment was established.

供试的6种杂草对该混剂的敏感性存在显著差异：红心藜Chenopodium album、龙葵Solanum nigrum和反枝苋Amaranthus retroflexus对该混剂最敏感，ED90值分别为47.65、71.67和29.17g/hm2；春蓼Polygonum persicaria和苘麻Abutilon theophrasti敏感，ED90值分别为96.91、114.20g/hm2；而番茄不敏感。