Summary of Simulation-based Inference with Quantile Regression, by He Jia
Simulation-Based Inference with Quantile Regression
by He Jia
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cosmology and Nongalactic Astrophysics (astro-ph.CO); Instrumentation and Methods for Astrophysics (astro-ph.IM); 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 The paper proposes Neural Quantile Estimation (NQE), a novel method for Bayesian inference based on conditional quantile regression. NQE learns individual one-dimensional quantiles for each posterior dimension, conditioned on the data and previous posterior dimensions. This allows for efficient sampling from the posterior distribution using monotonic cubic Hermite splines. The paper also introduces an alternative definition of the Bayesian credible region using local Cumulative Density Functions (CDF), which can be evaluated faster than traditional Highest Posterior Density Regions (HPDR). A calibration step is proposed to ensure unbiasedness in cases where simulation budgets are limited or model misspecification occurs. NQE achieves state-of-the-art performance on various benchmark problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to make predictions about data using something called Neural Quantile Estimation (NQE). It’s like taking a picture of the whole landscape, not just one part of it. The method uses special math to understand the shape of the data and can even correct mistakes made in how we think the data works. This helps us get more accurate results when making predictions. |
Keywords
* Artificial intelligence * Bayesian inference * Regression
