We consider the problem of learning structures and parameters of Continuous-time Bayesian Networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences. A popular approach to overcome this is Bayesian optimal experimental design (BOED). However, BOED becomes infeasible in high-dimensional settings, as it involves integration over all possible experimental outcomes. We propose a novel criterion for experimental design based on a variational approximation of the expected information gain. We show that for CTBNs, a semi-analytical expression for this criterion can be calculated for structure and parameter learning. By doing so, we can replace sampling over experimental outcomes by solving the CTBNs master-equation, for which scalable approximations exist. This alleviates the computational burden of sampling possible experimental outcomes in high-dimensions. We employ this framework in order to recommend interventional sequences. In this context, we extend the CTBN model to conditional CTBNs in order to incorporate interventions. We demonstrate the performance of our criterion on synthetic and real-world data.

翻译：我们从最小实验资源下从时段数据中考虑连续海湾网络的学习结构和参数问题。实际上,产生实验数据的成本是一个瓶颈,特别是在自然和社会科学方面。克服这一困难的流行办法是巴伊西亚最佳实验设计(BOED)。然而,在高维环境中,BED变得不可行,因为它涉及所有可能的实验结果的整合。我们提出了一个基于预期信息收益的变相近似值的实验设计新标准。我们表明,对于CTBN,这一标准的半分析表达方式可以用来计算结构和参数学习。这样,我们就可以通过解决CTBNs总和计算(存在可变近似值)来取代实验结果的抽样。这可以减轻高维值中可能的实验结果的计算负担。我们利用这个框架来建议干预序列。我们把CTBN模型扩展为有条件的CTBN,以便纳入干预。我们用它来证明我们的合成数据和实际数据标准的执行情况。