图 书 馆
图形模型Graphical models |
|
| 课程网址: | http://videolectures.net/mlss07_ghahramani_grafm/ |
| 主讲教师: | Zoubin Ghahramani |
| 开课单位: | 剑桥大学 |
| 开课时间: | 2007-08-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | An introduction to directed and undirected probabilistic graphical models, including inference (belief propagation and the junction tree algorithm), parameter learning and structure learning, variational approximations, and approximate inference. \\ - Introduction to graphical models: (directed, undirected and factor graphs; conditional independence; d-separation; plate notation) \\ - Inference and propagation algorithms: (belief propagation; factor graph propagation; forward-backward and Kalman smoothing; the junction tree algorithm) \\ - Learning parameters and structure: maximum likelihood and Bayesian parameter learning for complete and incomplete data; EM; Dirichlet distributions; score-based structure learning; Bayesian structural EM; brief comments on causality and on learning undirected models) \\ - Approximate Inference: (Laplace approximation; BIC; variational Bayesian EM; variational message passing; VB for model selection) \\ - Bayesian information retrieval using sets of items: (Bayesian Sets; Applications) \\ - Foundations of Bayesian inference: (Cox Theorem; Dutch Book Theorem; Asymptotic consensus and certainty; choosing priors; limitations) |
| 关 键 词: | 无向概率图模型; 近似推断; 贝叶斯推理 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-16：cjy |
| 阅读次数: | 32 |