动力系统种群初始条件的密度估计Density estimation of initial conditions for populations of dynamical systems |
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课程网址: | http://videolectures.net/aispds08_busetto_deic/ |
主讲教师: | Alberto Busetto |
开课单位: | 苏黎世理工学院 |
开课时间: | 信息不详。欢迎您在右侧留言补充。 |
课程语种: | 英语 |
中文简介: | 提出了一种估计动力系统总体初始条件概率密度的计算方法。它的范围扩展到一系列问题,其中包括所述的蛋白质降解示例。它允许提出假设,以证明单细胞和种群动态之间的差异。该方法基于预处理回归,允许领域知识的整合。这些知识是以先验信息的形式给出的,这些先验信息是关于单个细胞的轨迹和噪声观测的动态行为的。在类似的问题中,额外的知识可以作为优先考虑的功能。这种优势在纯数据驱动的方法中是不可能实现的,如果存在这种优势,就必须加以利用。在系统生物学中,化学反应通常被很好地理解,但复杂的系统或网络仍在研究中。然而,先验知识的集成代价很高,一般来说,计算推理的可行方法必须是近似的。 |
课程简介: | A computational approach that estimates the probability density of the initial conditions for a population of dynamical systems is presented. Its scope extends to a family of problems which includes the described protein degradation example. It permits the formulation of hypotheses that can justify the discrepancy between single-cell and population dynamics. The approach is based on a preprocessing regression that permits the incorporation of domain knowledge. This knowledge is given under the form of prior information about the trajectory of a single cell and about the dynamical behavior of the noisy observations. In similar problems, additional knowledge can be available as a prior over functions. This advantage is not possible with purely data-driven approaches and, when existing, it must be exploited. In systems biology, the chemical reactions are often understood quite well, but complex systems or networks are still under investigation. However, integration of prior knowledge comes with an high cost and, in general, feasible approaches to compute inference must be approximated. |
关 键 词: | 动力系统; 种群; 密度 |
课程来源: | 视频讲座网 |
最后编审: | 2019-10-30:cwx |
阅读次数: | 38 |