一个最大后验估计的方法来评估这些参数的估计的最优值。
A maximum a posteriori estimation approach is used to evaluate the optimal values for the estimates of the parameters.
文中侧重研究了两种以贝叶斯概率后验估计理论为基础的全局定位方法。
Two different global localization methods based on Bayesian estimation theory are mainly investigated in the paper.
设计了一个先验密度惩罚图像当中分水线变换后的相似的区域，图像分割进而变成对目标子集的最大后验估计。
The segmentation problem is the maximizing a posteriori estimation of the set of object area result from the watershed labeled.
采用最大似然估计或最大后验概率准则，用估计值来取代前面等式中的真实值。
Either the maximum likelihood estimate or the maximum a posteriori estimate may be used in place of the exact value in the above equations.
对于解码状态参数，通过计算最大后验转移概率的方法作最佳估计，井给出了一种简化的计算方法。
The codec state is also estimated by computing the maximum posterior transition probabilities, with a simplified computing method described.
对于解码状态参数，通过计算最大后验转移概率的方法作最佳估计，并给出了一种简化的估计方法。
The codec states were estimated by computing the maximum posterior transition probabilities with a simplified computing method.
分割问题可以被转换成一种最大后验概率估计问题。
Then the segmentation problem is formulated as Maximum a Posterior Probability (MAP) estimation rule.
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。
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.
后验误差估计是实现自适应有限元计算的关键性手段。
A posteriori error estimates serve as a key to realize the adaptive finite element computation.
并在此基础上讨论了后验误差估计。
采用贝叶斯最大后验概率估计的方式，从统一背景模型中生成说话人模型。
We use Bayesian maximum a posteriori estimation training a speaker model from background model, to solve the problem of model miss matching in speaker verification system.
更进一步地，因种类分布密度无法从那样的训练集中进行估计，种类的后验概率也无法被估计出来。
Moreover, as class density estimates cannot be derived for such a training set, class posterior probabilities cannot be estimated.
本文分析了四种基于局部量计算的恒定磁场后验误差估计方法。
In this paper, four methods for local error estimation in finite element solution are described and analyzed.
在模型估计上，采用等级似然估计方法，从而避免了求后验分布的积分运算，简化了估计过程。
Using hierarchical likelihood approach, the multidimensional integral is avoided, and the hierarchical likelihood function and the process of estimating model ar.
给出矩形域上弱奇异积分算子本征值问题分片零次多项式配置法的后验误差估计式。
The posteriori error estimators in the collocation method for integral equation eigenvalue problem with a weakly singular kernel are presented.
进一步研究了基于吉布斯抽样的贝叶斯最大后验概率方位估计方法。
Bayesian maximum a posterior DOA estimator based on Gibbs sampling (GSBM) is further investigated.
针对GPSRTK技术定位精度验后估计的欠缺，给出了GPS RTK测量成果的精度估计方法，通过实例介绍了此法的应用。
The methods of precision estimation of GPS RTK survey production are given in this paper, and their application is introduced with examples.
依据这一模型，该方法使用贝叶斯理论和领域约束获得了区域和边界的最大后验概率估计。
The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields(MRFs) models.
依据这一模型，该方法使用贝叶斯理论和领域约束获得了区域和边界的量大后验概率估计。
The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields (MRFs) models.
基于极大后验概率估计准则计算了位置偏差的估值。
The location disparity was estimated based on maximum a posteriori criterion.
针对传统的支持向量机方法不能提供后验概率的输出问题，从信息熵的角度采用最大熵估计方法，直接对支持向量机输出进行后验概率建模。
To the problem that the standard SVM does not provide probabilities output, the probabilistic outputs for support vector machines is modeled based on the maximum entropy estimation.
它是建立在阵列输出信号和噪声参数联合后验概率密度基础上的空间谱估计。
It is established on the expected value of the theoretical spatial spectrum over the joint posterior density function of the array output signal and noise parameters.
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。
Simultaneously, this method provides the posterior probability density and the error bars of estimated weights, which deduces the uncertainty of reconstruction.
提出了基于重要性抽样的贝叶斯最大后验概率方位估计方法。
Bayesian maximum a posterior DOA estimator based on importance sampling (ISBM) is proposed.
将后验均值作为贝叶斯估计值后，便得到损失频度与强度分布。
After posterior mean is exploited as Bayesian estimate, loss frequency distribution and severity distribution are gotten.
后验误差估计是自适应有限元分析的关键环节。
A posteriori error estimation is the key link of the adaptive finite element analysis.
取后验均值作为模糊点估计值后，便得到损失频度与强度分布。
After posterior mean is exploited as fuzzy point estimate, loss frequency distribution and severity distribution are gotten.
取后验均值作为模糊点估计值后，便得到损失频度与强度分布。
After posterior mean is exploited as fuzzy point estimate, loss frequency distribution and severity distribution are gotten.
应用推荐