先验概率分布 meaning in Chinese
prior probability distribution
Examples
- The algorithm needs no prior probability distributing knowledge of measurement data , and is easy to realize with simple programming and calculation
该算法不要求知道测量数据的任何先验概率分布知识，编程简单，计算量小。 - The prior information of pixel intense distribution is introduced . then simulated annealing algorithm is applied to choose the proper neighborhood structure , and the optimal estimate can be obtained
引入像素强度的先验概率分布模型，运用模拟退火算法选择合适的邻域结构，获得强度的最优估计。 - Compared with the regular rule - based expert system , the bayesian network based es can reason on the incomplete input information using the prior probability distribution ; the topological structure of the network being used to express the qualitative knowledge and the probability distributions of the nodes in the network being used to express the uncertainty of the knowledge , which made the knowledge representation more intuitively and more clearly ; applying the principle of the bayesian chaining rule , bidirectional inference which allow infer from the cause to the effect and from the effect to the cause can be achieved
与一般基于规则的专家系统相比，贝叶斯网专家系统利用先验概率分布，可以使推理在输入数据不完备的基础上进行；以网络的拓扑结构表达定性知识，以网络节点的概率分布表达知识的不确定性，从而使不确定性知识的表达直观、明确；利用贝叶斯法则的基本原理，可以实现由因到果及由果到因的双向推理。 - 3 , on the base of the traditional spatial filtering , the author present , a new despeckle algorithm , that make use of iterated processing and correlated neighbourhood model , iterated filtering method of the sar image combining the correlated neighbourhood model with maximum a posteriori filter . first , a series of templates refecting direction information are established and every template is present for a kind of neighbour structure . then on the basis of sar images statistical property , the maximum a posteriori estimate of the real intensity under observation image values is got by bayes formulatio - n
3 、针对传统空间滤波器的不足，引入迭代处理和相关邻域模型的概念，提出了基于相关邻域模型的最大后验迭代滤波。该算法引用一系列反映局部边界特征的邻域模型，以描述图像的细节。引入强度的先验概率分布模型，利用bayes方法，对各个结构进行实际强度的最大后验估计。