图 书 馆
马尔可夫跳跃过程的变分推理Variational Inference for Markov Jump Processes |
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| 课程网址: | http://videolectures.net/pim07_sanguinetti_vim/ |
| 主讲教师: | Guido Sanguinetti |
| 开课单位: | 谢菲尔德大学 |
| 开课时间: | 2007-11-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Markov jump processes (MJPs) underpin our understanding of many important systems in science and technology. They provide a rigorous probabilistic framework to model the joint dynamics of groups (species) of interacting individuals, with applications ranging from information packets in a telecommunications network to epidemiology and population levels in the environment. These processes are usually non-linear and highly coupled, giving rise to non-trivial steady states (often referred to as emerging properties). Unfortunately, this also means that exact statistical inference is unfeasible and approximations must be made in the analysis of these systems. A traditional approach, which has been very successful throughout the past century, is to ignore the discrete nature of the processes and to approximate the stochastic process with a deterministic process whose behaviour is described by a system of non-linear, coupled ODEs. This approximation relies on the stochastic fluctuations being negligible compared to the average population counts. There are many important situations where this assumption is untenable: for example, stochastic fluctuations are reputed to be responsible for a number of important biological phenomena, from cell differentiation to pathogen virulence. Researchers are now able to obtain accurate estimates of the number of macromolecules of a certain species within a cell, prompting a need for practical statistical tools to handle discrete data. |
| 关 键 词: | 马尔可夫跳跃过程; 概率框架; 非线性 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-01-13：chenxin |
| 阅读次数: | 67 |