Summary of Parameter Inference Via Differentiable Diffusion Bridge Importance Sampling, by Nicklas Boserup et al.
Parameter Inference via Differentiable Diffusion Bridge Importance Sampling
by Nicklas Boserup, Gefan Yang, Michael Lind Severinsen, Christy Anna Hipsley, Stefan Sommer
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 introduces a methodology for performing parameter inference in high-dimensional, non-linear diffusion processes. The authors demonstrate its applicability in obtaining insights into species evolution and relationships, including ancestral state reconstruction. They utilize score matching to approximate diffusion bridges, which are then used in an importance sampler to estimate log-likelihoods. This setup is differentiable, allowing gradient ascent on approximated log-likelihoods for both parameter inference and diffusion mean estimation. The framework is novel, numerically stable, and score matching-based. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how species change over time and relate to each other. Scientists can use this method to learn more about the evolution of different species and how they are connected. They do this by using a special kind of math called diffusion processes. This process helps them figure out the likelihood that certain species will evolve in certain ways. The authors also show that their method works well with real-life data from biology. |
Keywords
» Artificial intelligence » Diffusion » Inference » Likelihood
