Summary of Amortized Bayesian Workflow (extended Abstract), by Marvin Schmitt et al.
Amortized Bayesian Workflow (Extended Abstract)
by Marvin Schmitt, Chengkun Li, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, Stefan T. Radev
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes an adaptive workflow that balances computational speed and sampling accuracy for Bayesian inference. The approach integrates rapid amortized inference and gold-standard MCMC techniques to achieve both speed and accuracy when performing inference on many observed datasets. The workflow uses principled diagnostics to guide the choice of inference method for each dataset, moving along the Pareto front from fast amortized sampling to slower but guaranteed-accurate MCMC when necessary. By reusing computations across steps, the approach creates synergies between amortized and MCMC-based inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make Bayesian inference faster and better. It combines two ways of doing Bayesian inference – one that’s fast but not always accurate, and another that’s slower but more accurate. The combination makes it possible to do Bayesian inference quickly and accurately on many datasets at once. This is important because Bayesian inference has lots of applications in fields like climate science and medicine. |
Keywords
» Artificial intelligence » Bayesian inference » Inference
