Summary of Likelihood Approximations Via Gaussian Approximate Inference, by Thang D. Bui
Likelihood approximations via Gaussian approximate inference
by Thang D. Bui
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 A novel approach is presented to efficiently approximate the effects of non-Gaussian likelihoods in complex real-world observations, enabling the application of inference strategies originally designed for Gaussian models. The proposed method uses variational inference and moment matching in transformed bases to approximate Gaussian densities from non-Gaussian likelihoods, achieving good approximation quality for binary and multiclass classification tasks. The results demonstrate that the proposed methods outperform existing likelihood approximations and approximate inference methods in large-scale point-estimate and distributional inferential settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make complex real-world observations easier to work with by using a new way of thinking about likelihoods. It’s like taking a puzzle that’s hard to solve because it has many pieces, and finding a way to make it into a simpler puzzle that can be solved easily. This helps us learn and understand things better. The results are very promising, showing that this method works well for different types of problems. |
Keywords
* Artificial intelligence * Classification * Inference * Likelihood
