Summary of Distributed Mcmc Inference For Bayesian Non-parametric Latent Block Model, by Reda Khoufache et al.
Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model
by Reda Khoufache, Anisse Belhadj, Hanene Azzag, Mustapha Lebbah
First submitted to arxiv on: 1 Feb 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 The paper introduces a novel Distributed Markov Chain Monte Carlo (MCMC) inference method for the Bayesian Non-Parametric Latent Block Model (DisNPLBM), using the Master/Worker architecture. The approach divides observations and features into partitions using latent multivariate Gaussian block distributions, with evenly distributed workload on rows among workers. Experimental results demonstrate DisNPLBM’s impact on cluster labeling accuracy and execution times. A real-use case applies the approach to co-cluster gene expression data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to group similar things together (like genes that behave similarly) using complex math and computer code. It uses a special kind of computer architecture to make it work fast and accurate. The results show this method works well for grouping genes and is better than other methods in some ways. |
Keywords
* Artificial intelligence * Inference
