据此，建立了最大后验概率指标。
根据最大后验概率准则，选出最优的切分结果。
Finally, maximum a posterior (MAP) criterion is used to select the optimal segmentation result from all candidate segmentation results.
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.
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.
另外，还给出了一种计算符号序列后验概率的简单方法。
In addition, a simple approach for calculating the a posterior probabilities of symbol sequences is suggested.
图1地震发生在各组断层上的后验概率分布直方图fig。
Histogram of posterior probability distribution for earthquake to occur on various fault groups.
针对不平衡数据集，提出一种基于后验概率的特征选择算法。
In this paper, a posterior-probability-based feature selection algorithm is proposed for imbalanced datasets.
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.
贝叶斯方式是依据新的信息从先验概率得到后验概率的一种方式。
Bayesian is one kind of method of posteriori probability obtained from priori probability according to new information.
实验结果表明最大后验概率估计算法也能高效重建出高质量图像。
The experimental results show that the MAP algorithm can be able to efficiently reconstruct a high quality image.
经证明，在特殊参数时，背景抑制指标等价为特征匹配的后验概率。
It is proven that with specific parameter, Background Suppressed similarity measure is equivalent to the posterior probability of the matched feature.
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.
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。
In the process, we calculate the posterior probability of semantics by unlabeled samples information.
本文提出了一种孤立词语音识别系统中基于后验概率差值的拒识算法。
This paper proposes an algorithm based on likelihood difference for isolated-word speech recognition system.
提出了一种用于目标识别的多传感器雅息融合算法—后验概率检测算法。
A multiple sensor information fusion algorithms-posterior probability detection algorithms is presented and applied to target identification.
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.
根据条件随机场模型和马尔可夫随机场模型建立了一个最大后验概率框架。
Firstly, maximum a posteriori framework is created according to conditional random field model and Markov random field model.
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.
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。
A SVM model based on the posterior probability is bring forward by using a commercial bank's housing credit data.
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.
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。
The posterior probability is computed from the prior and the likelihood function via Bayes' theorem.
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。
The available methods for watermarking detection include related detection, maximum plausible detection and maximum posterior probability detection, etc.
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.
构造了一种基于并行优化算法的后验概率分析算法，用于对匹配场反演结果进行定量的概率分析。
A posteriori probability analysis method was proposed based on a parallel optimization algorithm, and applied to quantitative analysis of matched inversion results.
为了解决通道变化对说话人识别系统性能的影响，将最大后验概率方法应用到具体的通道补偿中。
To solve the effect of channel changes on the performance of speaker identification system, apply the method of maximum a posteriori to specific channel compensation.
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.
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。
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。
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.
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