Summary of Bindy — Bayesian Identification Of Nonlinear Dynamics with Reversible-jump Markov-chain Monte-carlo, by Max D. Champneys et al.
BINDy – Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo
by Max D. Champneys, Timothy J. Rogers
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Dynamical Systems (math.DS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Bayesian identification of nonlinear dynamics (BINDy) method offers a novel approach to dictionary learning system identification, distinct from traditional sparse identification of nonlinear dynamics (SINDy) methods. By targeting the full joint posterior distribution over both library functions and their parameterizations, BINDy allows for arbitrary priors on model structure, leading to models that are sparse in the model space rather than in parameter space. The method employs a Gibbs sampler based on reversible-jump Markov-chain Monte-Carlo (MCMC) inference. In three benchmark case-studies, BINDy is shown to outperform ensemble SINDy in terms of assigning high probability to correct model terms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand complex systems by learning about the underlying rules that govern their behavior. They call this method Bayesian identification of nonlinear dynamics (BINDy). Unlike previous methods, BINDy allows us to choose what we want the models to look like before we start learning from data. This helps to make the models simpler and easier to understand. The authors tested BINDy on three real-world examples and found that it worked better than another popular method called SINDy. |
Keywords
» Artificial intelligence » Inference » Probability
