Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control

TL;DR

This paper presents a novel Bayesian inference method using Sequential Monte Carlo for hybrid models in bioprocessing, enabling efficient learning, monitoring, and control under uncertainty with limited data.

Contribution

It introduces a linear Gaussian dynamic Bayesian network auxiliary likelihood approach to accelerate hybrid model inference in bioprocess mechanisms.

Findings

Efficient posterior estimation with limited observations

Enhanced process monitoring and control capabilities

Accelerated inference through LG-DBN likelihood approximation

Abstract

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.