图 书 馆
学习概率分布的多线性表示法进行有效推理Learning Multi-Linear Representations of Probability Distributions for Efficient Inference |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_samdani_lmlr/ |
| 主讲教师: | Rajhans Samdani |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We examine the class of multi-linear polynomial representations (MLR) for expressing probability distributions over discrete variables. Recently, MLR have been considered as intermediate representations that facilitate inference in distributions represented as graphical models. We show that MLR is an expressive representation of discrete distributions and can be used to concisely represent classes of distributions which have exponential size in other commonly used representations, while supporting probabilistic inference in time linear in the size of the representation. Our key contribution is presenting techniques for learning bounded-size distributions represented using MLR, which support efficient probabilistic inference. We propose algorithms for exact and approximate learning for MLR and, through a comparison with Bayes Net representations, demonstrate experimentally that MLR representations provide faster inference without sacrificing inference accuracy. |
| 关 键 词: | 离散变量; 概率分布; 多线性 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 75 |