Summary of Extended Fiducial Inference: Toward An Automated Process Of Statistical Inference, by Faming Liang et al.
Extended Fiducial Inference: Toward an Automated Process of Statistical Inference
by Faming Liang, Sehwan Kim, Yan Sun
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 proposes a new statistical inference method called extended Fiducial inference (EFI), which aims to infer the uncertainty of model parameters based on observations. The approach leverages advanced computing techniques, including stochastic gradient Markov chain Monte Carlo and sparse deep neural networks (DNNs). EFI achieves scalability for big data and offers advantages over frequentist and Bayesian methods in parameter estimation and hypothesis testing. Specifically, it provides higher fidelity in parameter estimation when outliers are present and eliminates the need for theoretical reference distributions in hypothesis testing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to figure out how sure we can be about things based on what we observe. It’s called extended Fiducial inference (EFI). The idea is to use computers really well to make predictions and understand how likely they are to be wrong. This helps us make better guesses about the world and makes it easier to do science. |
Keywords
* Artificial intelligence * Inference
