先验概率的英文

• a prior probability
• a priori probability
• apriori probability
• bayes theorem
• probability a priori

• "先验"英文翻译    prior; a priori
• "概率"英文翻译    probability; chance
• "先验概率分布" 英文翻译 :    prior probability distribution
• "客观先验概率分布" 英文翻译 :    objective prior probability distribution
• "后验概率" 英文翻译 :    a posterier probability; apa posteriori probability; bayes theorem; posterior probability; probability a posteriori
• "经验概率" 英文翻译 :    aposterior probability; aposteriori probability; detection probability; empircal probability; empiric probability; empirical probability
• "经验概率值" 英文翻译 :    empirical probity
• "实验概率" 英文翻译 :    experimental probability
• "经验概率单位,经验概率值" 英文翻译 :    empirical probit
• "后验概率分布" 英文翻译 :    posterior probability distribution
• "经验概率单位" 英文翻译 :    empirical probability
• "随机后验概率" 英文翻译 :    random posterior probability
• "最大后验概率" 英文翻译 :    maximum a posteriori probability
• "后验概率极大化" 英文翻译 :    map
• "最大后验概率估计" 英文翻译 :    maximum posterior probability estimation
• "反概率准则(后验概率准则)" 英文翻译 :    inverse probability criterion
• "后验概率极大化算法" 英文翻译 :    maalgorithm; map algorithm
• "先验" 英文翻译 :    [哲学] prior; a priori 先验知识 a priori knowledge; 先验论 apriorism; transcendentalism; 先验性 apriority
• "先验的" 英文翻译 :    a priori; apriori; transcendental
• "先验法" 英文翻译 :    a priori approach
• "先验集" 英文翻译 :    prior set
• "先验论" 英文翻译 :    a priorism; apriorism
• "先验权" 英文翻译 :    priore weight
• "先验说" 英文翻译 :    transcendentalism
• "先验性" 英文翻译 :    apriority

• 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。
• It is popularly considered that considering the influence of the prior probability accords with bayes rule and ensures the minimal loss in the classifying progress
  一般认为,考虑先验概率符合bayes准则,能够保证分类过程中错分的损失最小。
• In the process of decision with risks , if we using sampling theory , the best decision in prior probability and modified posteriori probability can be obtained
  在进行风险型决策过程中，若能结合抽样理论，就可以以最低的代价找到先验概率下及修正后的后验概率下选择最优决策方案。
• 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。
• In the probability model , we provide the case of client evaluation in the bank credit and utilize the some kinds of probabilities from statistics analysis and field experts to reasoning and information fusion
  概率推理模型中本文试举了银行信贷客户评价的推理融合案例，利用统计分析和领域专家给出的各种先验概率来进行推理和信息的融合。
• Supported by the analysis and advance process to the geographical data using gis software , the paper discusses the question that whether the accuracy of bayes supervised classification will be improved considering the influence of the prior probability
  本文尝试利用gis软件对地理数据进行分析和预处理,对考虑先验概率是否提高bayes监督分类精度这一问题作了探讨。
• The proportion based on the assistant data is used as the prior probability to replace the prior value in the conventional supervised classification ; the farther iterative prior probability is applied into classifying progress on landsat tm image
  由辅助数据中计算各类别面积比率作为先验概率,替换传统监督分类中的先验值,并进一步对先验概率进行迭代,最后利用改进的先验概率对landsattm影像进行分类实验。
• And to model the field - nonspecific information is to evaluate the accepted level of the new knowledge for students . " java tutorial " is taken as an example curriculum , which is disassembled into 298 knowledge items and the mastery of each knowledge item can be expressed into 4 levels . thereafter , partial order among these knowledge items is set up so as to ascertain the causality on bayesian networks
  论文中以《 java教程》为例，将该门课程划分成298个知识项，并为每个知识项定义了四个状态来表示学生对知识项的掌握程度，然后在这些知识项之间建立偏序关系以确定贝叶斯网络的因果推理关系，最后为每个知识项确定先验概率，这样就建立了覆盖型的贝叶斯网络学生模型。
• 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。
• Monte carlo is a method that approximately solves mathematic or physical problems by statistical sampling theory . when comes to bayesian classification , it firstly gets the conditional probability distribution of the unlabelled classes based on the known prior probability . then , it uses some kind of sampler to get the stochastic data that satisfy the distribution as noted just before one by one
  蒙特卡罗是一种采用统计抽样理论近似求解数学或物理问题的方法，它在用于解决贝叶斯分类时，首先根据已知的先验概率获得各个类标号未知类的条件概率分布，然后利用某种抽样器，分别得到满足这些条件分布的随机数据，最后统计这些随机数据，就可以得到各个类标号未知类的后验概率分布。

• 先验概率的法语：probabilité a priori
• 先验概率的日语：じぜんかくりつ
• 先验概率的韩语：사전 확률
• 先验概率的俄语：Априорная вероятность