图 书 馆
连续时间马尔可夫过程的近似推理Approximate inference for continuous time Markov processes |
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| 课程网址: | http://videolectures.net/aispds08_opper_aict/ |
| 主讲教师: | Manfred Opper |
| 开课单位: | 柏林工业大学 |
| 开课时间: | 2008-09-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Continuous time Markov processes (such as jump processes and diffusions) play an important role in the modelling of dynamical systems in many scientific areas. In a variety of applications, the stochastic state of the system as a function of time is not directly observed. One has only access to a set of nolsy observations taken at a discrete set of times. The problem is then to infer the unknown state path as best as possible. In addition, model parameters (like diffusion constants or transition rates) may also be unknown and have to be estimated from the data. While it is fairly straightforward to present a theoretical solution to these estimation problems, a practical solution in terms of PDEs or by Monte Carlo sampling can be very time consuming and one is looking for efficient approximations. I will discuss approximate solutions to this problem such as variational approximations to the probability measure over paths and weak noise expansions. |
| 关 键 词: | 连续时间; 马尔可夫; 近似推理 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-05：lxf |
| 阅读次数: | 29 |