Summary of An Efficient Solution to Hidden Markov Models on Trees with Coupled Branches, by Farzan Vafa and Sahand Hormoz
An efficient solution to Hidden Markov Models on trees with coupled branches
by Farzan Vafa, Sahand Hormoz
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP); Quantitative Methods (q-bio.QM); Methodology (stat.ME)
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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 extension of Hidden Markov Models (HMMs) to tree-like structures is proposed, enabling efficient modeling of coupled branches in sequential data. This approach addresses the complexity inherent in biological systems, where entities within the same lineage exhibit dependent characteristics. A dynamic programming algorithm is developed for solving likelihood, decoding, and parameter learning problems, scaling polynomially with the number of states and nodes. The method’s feasibility is demonstrated through application to simulated data, accompanied by self-consistency checks for validating model assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hidden Markov Models are powerful tools that help us understand complex patterns in data. In this paper, researchers developed a new way to use HMMs when the data has a tree-like structure and different branches are connected. This is important because many biological systems have these kinds of connections between related entities. The team created an efficient algorithm for analyzing this kind of data and tested it on simulated examples. |
Keywords
» Artificial intelligence » Likelihood
