Summary of Variational Bayesian Methods For a Tree-structured Stick-breaking Process Mixture Of Gaussians by Application Of the Bayes Codes For Context Tree Models, By Yuta Nakahara
Variational Bayesian Methods for a Tree-Structured Stick-Breaking Process Mixture of Gaussians by Application of the Bayes Codes for Context Tree Models
by Yuta Nakahara
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); 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 This paper proposes a novel variational Bayesian (VB) method to learn tree-structured stick-breaking process (TS-SBP) mixture models, which can represent hierarchical structures among mixture components. The TS-SBP model is a non-parametric Bayesian model that requires Markov chain Monte Carlo (MCMC) methods for inference, which are computationally expensive. To overcome this limitation, the authors develop a VB method with less computational cost by utilizing a subroutine from Bayes coding algorithms. The proposed method assumes finite tree width and depth and can efficiently calculate sums over all possible trees. Experimental results on a benchmark dataset confirm the computational efficiency of the VB method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to learn tree-like patterns in data using Bayesian models. It’s like building a family tree, but instead of people, it’s numbers and shapes that are related. The problem is that the usual way to do this takes too long on big datasets. So, the authors came up with a faster method that still gets good results. They tested their idea on some real data and showed that it works well. |
Keywords
» Artificial intelligence » Inference
