片语

phoneme posterior probability音素后验概率

Posterior odds probability事后机率

posterior r probability事后概率

posterior probability density distribution后验概率密度分布

Posterior probability density function后验概率

posterior probability SVM后验概率SVM

word posterior probability词后验概率

posterior marginal probability后验边缘概率

英汉例句

• unscented kf utilizes a set of definite samplings to approximate posterior probability density function, while particle filter uses random particles.

  粒子滤波器是用随机样本来近似状态后验概率密度函数，适用于任何非线性非高斯系统。

• the available methods for watermarking detection include related detection, maximum plausible detection and maximum posterior probability detection, etc.

  通常的水印检测策略主要有线形相关检测、最大似然检测、最大后验概率检测等策略。

• this method doesn′t need channel noise power. according to the distance between symbols on the constellation, the posterior probability of the received symbol is calculated.

  该算法不需信道噪声功率，根据星座图上符号间的距离计算接收符号的后验概率，然后得到译码所需要的初始对数似然比消息。

• monte carlo method can generate a large collection of models according to the posterior probability distribution and analyses and display the models with relative likelihood of model properties.

  蒙特卡洛法能根据后验概率分布产生大量的模型，并能用模型的相关似然性质来分析和呈现这些模型。

• given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the monte carlo markov chain (mcmc) sampling method.

  该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。

• the primary of bayesian estimation is to deduce the conditional distribution function of unknown variables on the data, which is also called posterior probability density function (ppdf).

  它是根据测量的数据推导未知变量的条件分布函数，也就是后验概率密度函数，而热传导反问题的数值解就是后验概率密度函数的数学期望。

• the posterior probability output of binary svm was generalized to multiclass svm. without iteration computing, this method improved prediction accuracy with fast computing.

  随后，将二值支持向量机的后验概率输出也推广到多分类问题，避免了使用迭代算法，在快速预测的前提下提高了预测準确率。

• an algorithm based on bayes posterior probability for passive location of multi-target by single observer is presented in this paper.

  本文提出了一种基于贝叶斯后验概率最大原理的机载单站多目标无源定位算法。

• a svm model based on the posterior probability is bring forward by using a commercial bank「s housing credit data.

  利用某商业银行的住房信贷数据构建了基于后验概率的支持向量机评估模型。

• third, the initial segmentation is refined scale by scale to get the final segmentation of the sar image based on the posterior probability of classification which is estimated by the mixture model.

  最后根据sar图像的统计性质，利用基于混合模型估计的分类后验概率将初始分割结果逐尺度进行细化得到sar图像的最终分割。

• it proves that the posterior probability about experiments makes up hemigroup then proves that the posterior probability of the order experiments and the accumulation experiments are the same.

  证明了后验概率关于试验构成半群，从而序贯试验与累积试验具有相同的后验概率。

• then the segmentation problem is formulated as maximum a posterior probability (map) estimation rule.

  分割问题可以被转换成一种最大后验概率估计问题。

• maximum posterior probability (map) algorithm was used to realize the super-resolution (sr) recovery.

  应用最大后验概率（map）算法实现超分辨率复原。

• histogram of posterior probability distribution for earthquake to occur on various fault groups.

  图1地震发生在各组断层上的后验概率分布直方图fig。

• using the posterior probability to guide the fusion rules」 design of high frequency subband coefficients, it fuses the coefficients differently according to edge or background region.

  利用该后验概率指导高频系数融合的规则，对边缘和背景区域进行不同的融合处理，以尽可能保留原始图像的重要特征；

• the location with the largest posterior probability is regarded as the location of the most probable change-point.

  对应着最大后验概率的位置被认为是发生变点的最可能位置。该方法在长江宜昌站的年径流资料系列上进行了应用。

• the posterior probability is computed from the prior and the likelihood function via bayes' theorem.

  先验概率与后验概率有不可分割的联系，后验概率的计算要以先验概率为基础。

• samples with associated weights are used to approximate posterior probability density distribution of survivability in order to forecast survivability at the next moment.

  通过重采样，用加权样本组合逼近生存性的真实后验概率分布，实现对下一时刻系统生存性的预测。

• particle filtering is from the probability and statistic point of view, using a collection of random samples with corresponding value to express the posterior probability density.

  该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。

• simultaneously, this method provides the posterior probability density and the error bars of estimated weights, which deduces the uncertainty of reconstruction.

  该方法同时还给出了权值估计的后验概率密度和误差条，从而获得权值最优值的不确定性测量。

• according to bayesian theory and bayes factor, the posterior probability of the calculation sample belonging to a model is calculated.

  根据贝叶斯理论和贝叶斯因子法，计算样本隶属于某种线型的后验概率；

• in the process, we calculate the posterior probability of semantics by unlabeled samples information.

  在计算的过程中，使用了未标记样本的信息计算语义出现的后验概率。