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

arXiv: https://arxiv.org/abs/2205.02410 · HTML: https://mlsanity.com/paper/2205.02410/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 3 of 3)

- [Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Control](https://mlsanity.com/paper/1909.04261/) (2019)
- [Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models](https://mlsanity.com/paper/1604.07949/) (2016)
- [Posterior concentration and fast convergence rates for generalized Bayesian learning](https://mlsanity.com/paper/2111.10243/) (2021)

## Built on by (most cited first, 2 of 2)

- [Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network](https://mlsanity.com/paper/2405.02783/) (2024)
- [A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example](https://mlsanity.com/paper/2505.03177/) (2025)

## Abstract (arXiv)

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.