| 1. | The origin of the concept of obtaining posterior probabilities with limited information is attributable to thomas bayes .
根据有限的信息得到的后定概率的概念最初是贝叶斯创造的。 |
| 2. | Mandarin digit string recognition based on segment model using posterior probability decoding
基于后验概率解码段模型的汉语语音数字串识别 |
| 3. | Second , with the posterior probability weighting of the uncertain parameters , an augmented system is obtained for the uncertainty model
其次，将此不确定性模型通过不确定参数的后验概率加权，得到其增广系统。 |
| 4. | The linguistic knowledge of putonghua pronunciation was effectively introduced into the calculation of hmm based log posterior probability
新算法在隐马尔科夫模型的对数后验概率算法基础上，引入普通话发音的语言学知识。 |
| 5. | Experiment results show that the proposed method achieves the better classification effect and the better posterior probability distribution than other methods
实验结果表明，与同类算法相比，所提出的基于最大熵估计的概率建模方法具有优良的性能。 |
| 6. | In this paper , we consider the concatenation of bose - chaudury - hocquenheim ( bch ) and repetition codes and present soft - decision decoding schemes based on a map ( maximum a posterior probability ) criterion
在本文中，我们考虑了bch码和重复码的级联编码，并且基于最大后验概率准则，提出了一种软判决解码方法。 |
| 7. | Combined with the prior distribution of the model parameters and water quality observation data , joint posterior probability function which stands for the distribution characters was obtained by bayes ' theorem
结合模型参数的先验分布和水质监测数据，通过贝叶斯定理计算获得了表征参数分布规律的联合后验概率密度函数。 |
| 8. | Bayesian method is used to calculate the posterior probability density function of the parameters for the model , and then monte carlo method is used to sample the model for getting the estimated values of the parameters
采用贝叶斯方法计算出模型参数的后验概率密度函数，通过蒙特卡罗方法时其进行采样来获得参数的佑计值。 |
| 9. | By constructing two secure posterior probability evaluation protocols to deal with discrete and numeric , or categorical and continuous attributes respectively , we attain the naive bayesian classifier without preamble
本文针对离散值属性情形和连续值属性情形分别构造了保持隐私的后验概率计算协议，最后获得安全的朴素贝叶斯分类器协议。 |
| 10. | Markov chain monte carlo simulation ( mcmc ) was taken to sample the posterior distribution to get the marginal posterior probability function of the parameters , and the statistical quantities such as the mathematic expectation were calculated
通过马尔科夫链蒙特卡罗模拟对后验分布进行了采样，获得了参数的后验边缘概率密度，并在此基础上获得了参数的数学期望等统计量。 |
