扩散模型的弱噪声近似推理 ][Weak noise approximate inference for diffusion models]

扩散模型的弱噪声近似推理 Weak noise approximate inference for diffusion models


课程网址:	http://videolectures.net/pim07_ruttor_wna/
主讲教师:	Andreas Ruttor
开课单位:	柏林理工大学
开课时间:	2007-11-06
课程语种:	英语
中文简介:	通过随机差异方程（SDE）对生化网络的随机动力学建模已成功地用作这种模型的统计推断的基础。由于基于蒙特卡罗的推理对于SDE来说可能是耗时的，因此我们建议采用不同的近似方法。我们的想法是，当每种类型的分子数量很大时，ditiion模型很好地适用于化学动力学。在此限制中，与漂移相比，数量波动也很小，导致小的延迟时间。这表明应用了弱噪声扩展。
课程简介:	The modelling of the Stochastic Kinetics of biochemical networks by stochastic dierential equations (SDE) has been successfully used as a basis for statistical inference for such models. Since Monte Carlo based inference can be time consuming for SDEs, we suggest a dierent approximate approach. The idea is that a diusion model applies well to chemical kinetics, when the number of molecules of each type is large. In this limit, also the number fluctuations are small leading to a small diusion term compared to the drift. This suggests the application of a weak noise expansion.
关键词:	随机差异方程; 生化网络; 随机动力学
课程来源:	视频讲座网
最后编审:	2019-09-13：lxf
阅读次数:	58

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