图 书 馆
自适应序贯贝叶斯变化点检测Adaptive Sequential Bayesian Change-point Detection |
|
| 课程网址: | http://videolectures.net/nipsworkshops09_turner_asbcpd/ |
| 主讲教师: | Ryan Turner |
| 开课单位: | 剑桥大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Nonstationarity, or changes in the generative parameters, are often a key aspect of real world time series, which comprise of many distinct parameter regimes. An inability to react to regime changes can have a detrimental impact on predictive performance. Change point detection (CPD) attempts to reduce this impact by recognizing regime change events and adapting the predictive model appropriately. As a result, it can be a useful tool in a diverse set of application domains including robotics, process control, and finance. CPD is especially relevant to finance where risk resulting from parameter changes is often neglected in models. For example, Gaussian copula models used in pricing collateralized debt obligations (CDOs) had two key flaws: assuming that subprime mortgage defaults have a fixed correlation structure, and using a point estimate of these correlation parameters learned from historical data prior to the burst of the real-estate bubble [1, 2]. Bayesian change point analysis avoids both of these problems by assuming a change point model of the parameters and integrating out the uncertainty in the parameters rather than using a point estimate. |
| 关 键 词: | 参数方案; 机器人; 变更点检测 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 74 |