Summary of Ensemble Transport Filter Via Optimized Maximum Mean Discrepancy, by Dengfei Zeng and Lijian Jiang
Ensemble Transport Filter via Optimized Maximum Mean Discrepancy
by Dengfei Zeng, Lijian Jiang
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Other Statistics (stat.OT)
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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 proposed ensemble-based filter method reconstructs the analysis step of particle filtering using a transport map, which directly transports prior particles to posterior particles. The transport map is constructed through an optimization problem that matches the expectation information of the approximated and reference posteriors. This method inherits accurate posterior estimation from particle filtering while improving robustness with a variance penalty term that prioritizes minimizing discrepancy between expectations. Numerical examples demonstrate the advantage of this method over the ensemble Kalman filter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to estimate what might happen next is developed. It uses an “analysis step” that helps get closer to the true answer by comparing two kinds of information. This makes it more accurate and better at dealing with noisy data. Some tests are run to show how well this method works compared to others. |
Keywords
* Artificial intelligence * Optimization
