| 1. | The fusion method for prior distribution in multi - sources of prior information
多源验前信息下先验分布的融合方法 |
| 2. | The bayesian classification and identification method based on normal - inverted wishart prior distribution
先验分布的贝叶斯分类识别方法研究 |
| 3. | The concrete example indicates that the priors and classes chosen by this method are robust
实例表明，由该方法得到的先验分布及先验分布族具有较好的稳健性。 |
| 4. | The method of using history test datum to make prior distribution and d ? s evidence fusion theory are discussed
讨论了由历史试验数据确定先验分布的方法和多源信息的d ? s证据融合方法。 |
| 5. | By using the theory of the likelihood ratio test , a method for choosing prior distributions and classes of prior distribution is given
摘要利用似然比检验原理，给出了选取先验分布及先验分布族的似然比检验方法。 |
| 6. | Conclusion the different methods for estimation of prior parameter should be used according to known conditions and practical situation
结论在实际工作中，应根据已知条件和具体情况决定采用何种方法计算先验分布参数。 |
| 7. | Results under the condition of the conjugate prior distribution , the prior parameters computed by three methods were similar
结果在共轭先验分布的条件下，先验矩、分位数、众数与分位数三种方法确定的先验分布参数结果一致。 |
| 8. | 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。 |
| 9. | The central works of this paper are followed : the common expression forms of expert information and the method of using max entropy and optimization method to make prior distribution of expert information are presented
本文的主要工作如下:给出了专家信息常用的表达形式,采用最大熵方法及最优化方法给出专家信息的先验分布。 |
| 10. | What is much difficult in bayesian method is that the detenninations of prior are only some guidelines without complete and operationai theorem , and it ' s hard to value the justice and accuracy of a prior in manyfconditions
贝叶斯方法遇到的一个重大的问题是先验分布的确定依据的只是一些准则，没有可操作的完整的理论在许多情况下先验分布的合理性和准确性难以评价 |
