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使用矩闭方法的参数估计

Parameter estimation using moment-closure methods
课程网址: http://videolectures.net/licsb08_gillespie_peu/  
主讲教师: Colin Gillespie
开课单位: 纽卡斯尔大学
开课时间: 2008-04-17
课程语种: 英语
中文简介:
这张海报将解决系统生物学新科学中的一个关键问题:推断复杂随机动力学生化指标的速率参数网络模型,使用系统的部分,离散和噪声时间过程测量。尽管对精确随机模型的推断是可能的,但对于相对较小的网络而言,它是计算密集型的。我们使用近似值来探索随机动力学速率参数的贝叶斯估计模型,基于潜在随机过程的矩闭合分析。通过假设一个高斯分布,并使用前两个时刻的矩闭估计,我们可以大大提高参数推断的速度。参数空间可以有效通过将此近似嵌入到MCMC过程中进行探索。
课程简介: This poster will give tackle one of the key problems in the new science of systems biology: inference for the rate parameters underlying complex stochastic kinetic biochemical network models, using partial, discrete, and noisy time-course measurements of the system state. Although inference for exact stochastic models is possible, it is computionally intensive for relatively small networks. We explore Bayesian estimation of stochastic kinetic rate parameters using approximate models, based on moment closure analysis of the underlying stochastic process. By assuming a Gaussian distribution and using moment-closure estimates of the first two-moments, we can greatly increase the speed of parameter inference. The parameter space can be efficiently explored by embedding this approximation into an MCMC procedure.
关 键 词: 复杂随机动力学; 速率参数网络模型; 计算密集型; 参数空间
课程来源: 视频讲座网
最后编审: 2020-06-29:cxin
阅读次数: 28