Summary of Bayesian Circular Regression with Von Mises Quasi-processes, by Yarden Cohen et al.
Bayesian Circular Regression with von Mises Quasi-Processes
by Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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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 family of regression models for predicting circular values, which is crucial in various scientific fields. The proposed models are based on Gaussian processes targeting two Euclidean dimensions conditioned on the unit circle, and they have connections with continuous spin models in statistical physics. Unlike previous approaches, these models use maximum-entropy density and do not require wrapping or radial marginalization. To facilitate posterior inference, the authors introduce a new augmentation method that enables fast Gibbs sampling. They also demonstrate the effectiveness of their approach by applying it to two real-world problems: predicting wind directions and the percentage of the running gait cycle as a function of joint angles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to predict circular values, which is important in many scientific fields. The idea is based on a type of mathematical model called a Gaussian process, but with some special features that make it well-suited for predicting circular values. The authors also introduce a new method for doing calculations with these models, which makes it faster and easier to use them. They test their approach by using it to predict two different things: the direction of the wind and the percentage of time spent in each part of the gait cycle. |
Keywords
* Artificial intelligence * Inference * Regression
