Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control. (arXiv:2205.02410v3 [stat.ML] UPDATED)

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 …

arxiv bayesian bayesian inference hybrid importance inference learning ml sampling support