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PAC-贝叶斯分析：推理与统计物理学之间的联系PAC-Bayesian Analysis: A Link Between Inference and Statistical Physics |
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| 课程网址: | http://videolectures.net/cyberstat2012_seldin_pac_bayesian/ |
| 主讲教师: | Yevgeny Seldin |
| 开课单位: | 哥本哈根大学 |
| 开课时间: | 2012-10-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | PAC-Bayesian analysis is a general tool for deriving generalization bounds for a wide class of inference rules. Interestingly, PAC-Bayesian generalization bounds take a form of a trade-off between the empirical performance of the inference rule and the KL-divergence between the posterior distribution over the hypothesis space applied by the inference rule and a prior distribution over the hypothesis space. This form of a trade-off is closely related to the free energy in statistical physics. Moreover, PAC-Bayesian bounds can be used in order to determine the right "temperature" at which the system should be analyzed given a finite sample. In other words, PAC-Bayesian analysis introduces a principled way of treating finite samples in application of methods from statistical physics to inference. We present a generalization of PAC-Bayesian analysis to martingales. This generalization makes it possible to apply PAC-Bayesian analysis to time-evolving processes, including importance-weighted sampling, reinforcement learning, and many other domains. |
| 关 键 词: | PAC贝叶斯分析; 自由能; 统计物理学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-06：王勇彬(课程编辑志愿者) |
| 阅读次数: | 234 |