Summary of Accelerated Markov Chain Monte Carlo Using Adaptive Weighting Scheme, by Yanbo Wang et al.
Accelerated Markov Chain Monte Carlo Using Adaptive Weighting Scheme
by Yanbo Wang, Wenyu Chen, Shimin Shan
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper explores a novel approach to Gibbs sampling, a widely used MCMC algorithm. The authors introduce a random scan Gibbs sampling method that selects each latent variable non-uniformly, building upon recent advancements in the field. They demonstrate that this approach leaves the target posterior distribution invariant and derive an analytic solution for determining the selection probability. The proposed algorithm relies on choosing variables based on their marginal probabilities to enhance the mixing time of the Markov chain. Experimental results validate the effectiveness of the proposed Gibbs sampler on real-world applications, including benchmark datasets and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use a machine learning tool called Gibbs sampling. It’s a simple and efficient method that helps us find patterns in data. The authors are trying to make it even better by letting it choose which variables to update next in a more clever way. They show that this new approach works just as well as the old one, and they figure out how to decide when to update each variable. This could help us solve problems faster or with less computing power. |
Keywords
» Artificial intelligence » Machine learning » Probability
