Dynamic Bayesian Network Auxiliary ABC-SMC for Hybrid Model Bayesian Inference to Accelerate Biomanufacturing Process Mechanism Learning and Robust Control

• URL: http://arxiv.org/abs/2205.02410v2
• Date: Mon, 9 May 2022 15:55:35 GMT
• Title: Dynamic Bayesian Network Auxiliary ABC-SMC for Hybrid Model Bayesian Inference to Accelerate Biomanufacturing Process Mechanism Learning and Robust Control
• Authors: Wei Xie, Keqi Wang, Hua Zheng, Ben Feng
• Abstract summary: We present a knowledge graph hybrid model characterizing complex causal interdependencies of underlying bioprocessing mechanisms. It can faithfully capture the important properties, including nonlinear reactions, partially observed state, and nonstationary dynamics. We derive a posterior distribution model uncertainty, which can facilitate mechanism learning and support robust process control.
• Score: 2.727760379582405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driven by the critical needs of biomanufacturing 4.0, we present a probabilistic knowledge graph hybrid model characterizing complex spatial-temporal causal interdependencies of underlying bioprocessing mechanisms. It can faithfully capture the important properties, including nonlinear reactions, partially observed state, and nonstationary dynamics. Given limited process observations, we derive a posterior distribution quantifying model uncertainty, which can facilitate mechanism learning and support robust process control. To avoid evaluation of intractable likelihood, Approximate Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC) is developed to approximate the posterior distribution. Given high stochastic and model uncertainties, it is computationally expensive to match process output trajectories. Therefore, we propose a linear Gaussian dynamic Bayesian network (LG-DBN) auxiliary likelihood-based ABC-SMC algorithm. Through matching observed and simulated summary statistics, the proposed approach can dramatically reduce the computation cost and improve the posterior distribution approximation.

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