图 书 馆
图形模型的贝叶斯推理Bayesian Reasoning with Graphical Models |
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| 课程网址: | http://ocw.upm.es/ciencia-de-la-computacion-e-inteligencia-artifi... |
| 主讲教师: | Concha Bielza; Pedro Larrañaga |
| 开课单位: | 马德里理工大学 |
| 开课时间: | 2008-11-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This course motivates and introduces graphical models (with special attention to Bayesian networks) as well consolidated and popular tools with the ability to represent knowledge under uncertainty and reason with it, one of the main challenges in building intelligent systems in Artificial Intelligence. Uncertainty is modelled with probability theory and reasoning is based on Bayes’ rule. Bayesian networks represent factorizations of joint probability distributions. Nodes represent the variables of the domain and links represent the properties of conditional dependences and independences among the variables. The course will provide an in-depth exposition of theoretical and practical underpinnings. The course starts explaining the meaning of these networks to model both causal and non-causal knowledge under uncertainty, and both from a structural viewpoint (qualitative) and from a parametric viewpoint (quantitative). The following step is to query the network about different issues of interest, i.e. to make inferences from evidence that is being gathered. For example, we can ask for the diagnosis of a disease or for the most probable explanation of the observed evidence. The inference algorithms can obtain an exact or an approximate answer, the latter being computed via e.g. Monte Carlo simulation. The network is built with the aid of a domain expert, but it can also be induced from a database. This calls modern learning algorithms including parameter learning and structure learning techniques. Finally, additional topics include a number of successful real-world applications in different areas. |
| 关 键 词: | 图形模型; 贝叶斯网络; 推理算法 |
| 课程来源: | 马德里理工大学公开课 |
| 最后编审: | 2015-12-06：linxl |
| 阅读次数: | 61 |