Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models
Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models
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DOI：
10.1007/s10994-012-5326-3
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发表时间：
2013-04-01
期刊：
影响因子：
7.5
通讯作者：
Husmeier, Dirk
中科院分区：
文献类型：
--
作者：
Grzegorczyk, Marco;Husmeier, Dirk
为了放宽经典动态贝叶斯网络的同质性假设，近年来的各种研究将动态贝叶斯网络与多个变点过程结合起来。潜在的假设是，与由多个变化点划分的时间序列段相关的参数是先验独立的。在弱正则性条件下，参数可以在似然中积分，得到边际似然的封闭表达式。然而，在许多现实世界的应用中，先验独立性的假设是不现实的，在这些应用中，相互依赖的量之间特定于片段的调节关系倾向于经历渐进的进化适应。因此，我们提出了一种贝叶斯耦合方案来引入特定段交互参数之间的系统信息共享。我们研究了这种模型改进对逆向工程背景下网络重建准确性的影响，其目标是从时间基因表达谱中学习基因调控网络的结构。本文件的目的是在六个重要方面扩展和改进早先的会议文件。首先，我们提供了一个更全面和独立的方法说明。其次，我们通过在模型层次结构中引入额外的一层来扩展模型，从而允许网络节点之间的信息共享，并比较了噪声方差超参数的各种耦合方案。第三，我们引入了一种新的折叠Gibbs采样步骤，取代了原始MCMC算法中效率较低的非折叠Gibbs采样步骤。第四，我们展示了如何使用坍缩和阻塞技术来开发一种新的先进的MCMC算法，该算法显著提高了收敛性和混合性。第五，我们系统地研究了模型的（超）超参数的影响。第六，我们将所提出的全局信息耦合方案与基于顺序信息共享的备选范式进行了实证比较。
To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying assumption is that the parameters associated with time series segments delimited by multiple changepoints are a priori independent. Under weak regularity conditions, the parameters can be integrated out in the likelihood, leading to a closed-form expression of the marginal likelihood. However, the assumption of prior independence is unrealistic in many real-world applications, where the segment-specific regulatory relationships among the interdependent quantities tend to undergo gradual evolutionary adaptations. We therefore propose a Bayesian coupling scheme to introduce systematic information sharing among the segment-specific interaction parameters. We investigate the effect this model improvement has on the network reconstruction accuracy in a reverse engineering context, where the objective is to learn the structure of a gene regulatory network from temporal gene expression profiles. The objective of the present paper is to expand and improve an earlier conference paper in six important aspects. Firstly, we offer a more comprehensive and self-contained exposition of the methodology. Secondly, we extend the model by introducing an extra layer to the model hierarchy, which allows for information-sharing among the network nodes, and we compare various coupling schemes for the noise variance hyperparameters. Thirdly, we introduce a novel collapsed Gibbs sampling step, which replaces a less efficient uncollapsed Gibbs sampling step of the original MCMC algorithm. Fourthly, we show how collapsing and blocking techniques can be used for developing a novel advanced MCMC algorithm with significantly improved convergence and mixing. Fifthly, we systematically investigate the influence of the (hyper-)hyperparameters of the proposed model. Sixthly, we empirically compare the proposed global information coupling scheme with an alternative paradigm based on sequential information sharing.