Summary of Diffusion Posterior Sampling For Simulation-based Inference in Tall Data Settings, by Julia Linhart et al.
Diffusion posterior sampling for simulation-based inference in tall data settings
by Julia Linhart, Gabriel Victorino Cardoso, Alexandre Gramfort, Sylvain Le Corff, Pedro L. C. Rodrigues
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 The paper presents a novel approach to determine the best parameters for non-linear models that describe experimental data, which is crucial in complex large-scale simulators. The likelihood of these models is typically intractable, making classical MCMC methods unusable. The proposed method, simulation-based inference (SBI), uses deep generative models to approximate the posterior distribution relating input parameters to observations. This paper extends SBI to handle tall data, where multiple observations are available, by leveraging recent developments from score-based diffusion literature. The proposed method is compared to competing approaches on numerical experiments, demonstrating its superiority in terms of numerical stability and computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a big problem in science: figuring out the right parameters for complex models that describe experimental data. This is important because we have really powerful computers now that can simulate lots of things, but it’s hard to figure out what these simulations mean. The usual way to solve this problem doesn’t work well with complex models, so scientists are trying a new approach called simulation-based inference (SBI). SBI uses special computer programs to learn from simulated data and make predictions about real-world observations. This paper makes SBI better by allowing it to handle lots of observations at once, which is useful for many scientific problems. |
Keywords
» Artificial intelligence » Diffusion » Inference » Likelihood
