摘要翻译：
本文提出了一种新的动态遗传网络推理方法，使其能够面对比基因数P小得多的时间测量数n。该方法基于低阶条件依赖图的概念，我们在这里将其推广到动态贝叶斯网络中。我们的大部分结果都是基于与有向无环图相关的图模型理论。这样，我们定义了一个最小DAG G，它精确地描述了给定过程过去的全序条件依赖关系。然后，针对大p和小n估计的情况，我们提出了通过考虑低阶条件无关性来逼近DAG G。引入了部分q阶条件依赖DAG G(q)，并分析了它们的概率性质。一般说来，DAGs G(q)与DAG G不同，但仍然反映了遗传网络等稀疏网络的相关依赖事实。利用这种近似，我们提出了一种非贝叶斯推理方法，并证明了这种方法在模拟和实际数据分析中的有效性。推理过程是在R包'G1DBN'中实现的，可从CRAN档案中免费获得。
---
英文标题：
《Inferring dynamic genetic networks with low order independencies》
---
作者：
Sophie L\`ebre (SG)
---
最新提交年份：
2009
---
分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
--
一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
--

---
英文摘要：
  In this paper, we propose a novel inference method for dynamic genetic networks which makes it possible to face with a number of time measurements n much smaller than the number of genes p. The approach is based on the concept of low order conditional dependence graph that we extend here in the case of Dynamic Bayesian Networks. Most of our results are based on the theory of graphical models associated with the Directed Acyclic Graphs (DAGs). In this way, we define a minimal DAG G which describes exactly the full order conditional dependencies given the past of the process. Then, to face with the large p and small n estimation case, we propose to approximate DAG G by considering low order conditional independencies. We introduce partial qth order conditional dependence DAGs G(q) and analyze their probabilistic properties. In general, DAGs G(q) differ from DAG G but still reflect relevant dependence facts for sparse networks such as genetic networks. By using this approximation, we set out a non-bayesian inference method and demonstrate the effectiveness of this approach on both simulated and real data analysis. The inference procedure is implemented in the R package 'G1DBN' freely available from the CRAN archive.
---
PDF链接：
https://arxiv.org/pdf/704.2551