Summary of Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model Of Partially Observed Stochastic Reaction Network, by Wandi Xu and Wei Xie
Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Network
by Wandi Xu, Wei Xie
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper develops an efficient Bayesian inference approach for partially observed enzymatic stochastic reaction networks, a fundamental building block of multi-scale bioprocess mechanistic models. The proposed method addresses the challenges posed by nonlinear stochastic differential equations-based mechanistic models with partially observed states and measurement errors. A Bayesian updating linear noise approximation metamodel is introduced to approximate the likelihood of observations, incorporating structure information from the mechanistic model. An efficient posterior sampling approach is then developed using gradients of the derived likelihood to speed up Markov Chain Monte Carlo convergence. The empirical study demonstrates promising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve online learning for biomanufacturing processes by developing a new way to analyze complex systems. Bioprocesses involve many interacting components, like enzymes and chemical reactions. The challenge is that these interactions are hard to model accurately. The proposed method uses Bayesian inference, which is a statistical approach to make predictions based on incomplete data. It’s an interpretable approach that takes into account the structure of the system being modeled. The results show that this approach performs well in predicting the behavior of bioprocesses. |
Keywords
» Artificial intelligence » Bayesian inference » Likelihood » Online learning
