Summary of Compositional Simulation-based Inference For Time Series, by Manuel Gloeckler et al.
Compositional simulation-based inference for time series
by Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents an Amortized Simulation-Based Inference (SBI) method that trains neural networks on simulated data for Bayesian inference. The proposed approach leverages Markovian simulators, which emulate real-world dynamics through thousands of single-state transitions over time. By locally identifying parameters consistent with individual state transitions, the method can scale to time series data and improve simulation efficiency compared to directly estimating global posteriors. The paper demonstrates the effectiveness of this approach for neural posterior score estimation and likelihood estimation on various benchmark tasks and simulators in ecology and epidemiology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use computer simulations to make better predictions. It’s like using a video game to practice real-life situations, but instead of playing a game, scientists can train special computers to learn from the simulations. This helps them make more accurate predictions without having to do as many complex calculations. The method is especially useful for studying complex systems like weather patterns or disease spread. The researchers tested their approach on different types of data and showed that it’s faster and more accurate than other methods. |
Keywords
» Artificial intelligence » Bayesian inference » Inference » Likelihood » Time series
