Summary of An Efficient Wasserstein-distance Approach For Reconstructing Jump-diffusion Processes Using Parameterized Neural Networks, by Mingtao Xia et al.
An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
by Mingtao Xia, Xiangting Li, Qijing Shen, Tom Chou
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Probability (math.PR); Applications (stat.AP); Methodology (stat.ME)
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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 analyzes the Wasserstein distance between two probability distributions associated with multidimensional jump-diffusion processes, providing upper and lower bounds for discrepancies in their drift, diffusion, and jump amplitude functions. A temporally decoupled squared W2-distance method is proposed to efficiently reconstruct unknown jump-diffusion processes from data using parameterized neural networks, which can be enhanced by incorporating prior information on the drift function. The method’s performance is demonstrated across various examples and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to compare two types of complex mathematical models that describe how things change over time. It focuses on a special type of distance between these models, called the Wasserstein distance. The researchers find ways to make this distance easier to calculate and use it to build more accurate models from data. This can be useful for predicting things like stock prices or weather patterns. |
Keywords
» Artificial intelligence » Diffusion » Probability
