论文标题
混合模型贝叶斯推断的顺序重要性抽样，以支持生物普应机制学习和稳健控制
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control
论文作者
论文摘要
在生物制造4.0的关键需求的驱动下，我们引入了一种概率知识图杂交模型，该模型表征了对生物过程机制的风险和科学理解。它可以忠实地捕获重要特性，包括非线性反应，部分观察到的状态和非平稳动态。考虑到非常有限的实际过程观测值，我们得出了后验分布量化模型估计不确定性。为了避免评估顽固的似然，使用顺序蒙特卡洛（ABC-SMC）的近似贝叶斯计算采样可用于近似后验分布。在高随机和模型不确定性下，匹配输出轨迹在计算上昂贵。因此，我们创建了一个线性高斯动态贝叶斯网络（LG-DBN）基于辅助可能性的ABC-SMC方法。通过与可以捕获关键相互作用和变化的LG-DBN可能性驱动的汇总统计数据，所提出的算法可以加速混合模型推断，支持过程监测并促进机制学习和稳健的控制。
Driven by the critical needs of biomanufacturing 4.0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of bioprocess mechanisms. It can faithfully capture the important properties, including nonlinear reactions, partially observed state, and nonstationary dynamics. Given very limited real process observations, we derive a posterior distribution quantifying model estimation uncertainty. To avoid the evaluation of intractable likelihoods, Approximate Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC) is utilized to approximate the posterior distribution. Under high stochastic and model uncertainties, it is computationally expensive to match output trajectories. Therefore, we create a linear Gaussian dynamic Bayesian network (LG-DBN) auxiliary likelihood-based ABC-SMC approach. Through matching the summary statistics driven through LG-DBN likelihood that can capture critical interactions and variations, the proposed algorithm can accelerate hybrid model inference, support process monitoring, and facilitate mechanism learning and robust control.
