Summary of A Short Information-theoretic Analysis Of Linear Auto-regressive Learning, by Ingvar Ziemann
A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning
by Ingvar Ziemann
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Machine Learning (stat.ML)
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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 an information-theoretic proof of the consistency of the Gaussian maximum likelihood estimator in linear auto-regressive models. The proof demonstrates nearly optimal non-asymptotic rates for recovering model parameters without relying on stability assumptions or finite hypothesis classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study proves that a type of machine learning algorithm, called the Gaussian maximum likelihood estimator, is consistent and efficient in predicting patterns in data from linear auto-regressive models. This means the algorithm can accurately recover the underlying parameters of the model even with limited training data. |
Keywords
» Artificial intelligence » Likelihood » Machine learning
