图 书 馆
马尔可夫链的贝叶斯分析Bayesian Analysis of Markov Chains |
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| 课程网址: | http://videolectures.net/nips09_diaconis_bamc/ |
| 主讲教师: | Persi Diaconis |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Suppose we observe data and want to test if it comes from a Markov chain. If it does, we may want to estimate the transition operator. Working in a Bayesian way, we have to specify priors and compute posteriors. Interesting things happen if we want to put priors on reversible Markov chains. There are useful connections with reinforced random walk (work with Silke Rolles). On large-scale application to protein folding will be described. More generally, these problems arise in approximating a dynamical system by a Markov chain. For continuous state spaces, the usual conjugate prior analysis breaks down. Thesis work of Wai Liu (Stanford) gives useful families of priors where computations are "easy." These seem to work well in test problems and can be proved consistent. |
| 关 键 词: | 马尔可夫链; 转换运算符; 近似动力系统 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-24：cwx |
| 阅读次数: | 49 |