图 书 馆
基于群体蒙特卡罗方法的酶控制过程系统辨识System Identification of Enzymatic Control Processes Using Population Monte Carlo Methods |
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| 课程网址: | http://videolectures.net/pesb07_calderhead_sio/ |
| 主讲教师: | Ben Calderhead |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2007-04-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We demonstrate the superiority of Population Monte Carlo techniques over standard Metropolis Markov Chain Monte Carlo (MCMC) methods for inferring optimal parameters for a particular mechanistic model of a biological process given noisy experimental data. As our understanding of biological processes increases, the proposed models to describe them become more complex. With such potentially large numbers of equations and parameters, it is no longer feasible to hand-pick parameter values and be sure that the most appropriate values have been chosen. Monte Carlo methods are becoming more widely used for estimating parameter values, however we show that the standard Metropolis MCMC approach fails to converge on optimal values for even relatively simple models and that a more sophisticated method, in the form of non-Markovian Population Monte Carlo, may be successfully employed to produce consistent and accurate results. We illustrate the basic problem using the minimal model for the circadian genetic network in Arabidopsis thaliana, which consists of 3 linked differential equations containing a total of 6 parameters, with an additional noise parameter incorporated to estimate the variance of noise in the data. Joint work with Mark Girolami. |
| 关 键 词: | 群体蒙特卡罗技术; 噪声实验; 机械模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-13：lxf |
| 阅读次数: | 77 |