图 书 馆
狄利克雷过程和非参数贝叶斯建模Dirichlet Processes and Nonparametric Bayesian Modelling |
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| 课程网址: | http://videolectures.net/mlss06au_tresp_dpnbm/ |
| 主讲教师: | Volker Tresp |
| 开课单位: | 西门子公司 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Bayesian modeling is a principled approach to updating the degree of belief in a hypothesis given prior knowledge and given available evidence. Both prior knowledge and evidence are combined using Bayes' rule to obtain the a posterior hypothesis. In most cases of interest to machine learning, the prior knowledge is formulated as a prior distribution over parameters and the evidence corresponds to the observed data. By applying Bayes' formula we can perform inference about new data. Having observed sufficient data, the a posteriori parameter distribution is increasingly concentrated and the influence of the prior distribution diminishes. Under some assumptions (in particular that the likelihood model is correct and that the true parameters have positive a priori probability), the a posteriori distribution converges to a point distribution located at the true parameters. The challenges in Bayesian modeling are, first, to find suitable application specific statistical models and, second, to (approximately) solve the resulting inference equations. |
| 关 键 词: | 贝叶斯建模; 先验知识; 后验参数; 特定应用统计模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-16：cjy |
| 阅读次数: | 98 |