Summary of Efficient, Multimodal, and Derivative-free Bayesian Inference with Fisher-rao Gradient Flows, by Yifan Chen and Daniel Zhengyu Huang and Jiaoyang Huang and Sebastian Reich and Andrew M. Stuart
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
by Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS); Numerical Analysis (math.NA)
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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 The paper proposes a method for efficient approximate sampling from probability distributions known up to normalization constants. The approach is designed to address computational challenges in Bayesian inference for large-scale inverse problems in science and engineering applications. Specifically, it tackles three issues: repeated evaluations of expensive forward models, potential existence of multiple modes, and feasibility of gradient or adjoint solver for the forward model. By leveraging efficient approximate sampling, the proposed methodology can enable more effective solution of these complex problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to quickly and accurately sample from probability distributions when all we know is how likely something is compared to others. This helps solve big science and engineering problems that require lots of computations. The main challenges are doing many repeated calculations, dealing with multiple solutions, and figuring out the direction of change. By solving these issues, the method can make it easier to find answers. |
Keywords
» Artificial intelligence » Bayesian inference » Probability
