Summary of Estimating Unknown Population Sizes Using the Hypergeometric Distribution, by Liam Hodgson and Danilo Bzdok
Estimating Unknown Population Sizes Using the Hypergeometric Distribution
by Liam Hodgson, Danilo Bzdok
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); 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 In this research paper, a novel approach is proposed to estimate discrete distributions when both the total population size and the sizes of its constituent categories are unknown. The authors tackle this challenge using the hypergeometric likelihood, which accounts for severe under-sampling. This method is developed to model count data and demonstrated to outperform other likelihood functions in terms of accuracy of population size estimation and learning an informative latent space. Applications of this approach include inferring and estimating the complexity of latent vocabularies in text excerpts and accurately recovering the true number of gene transcripts from sparse single-cell genomics data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem by finding a way to estimate distributions when we don’t know how many things are in each group. It does this using a special math formula called hypergeometric likelihood. This method is good at handling situations where not all the groups have an equal number of things. The researchers tested their approach and found it works better than other methods for estimating population sizes and understanding hidden patterns in data. They also showed how this technique can be used to analyze text and genetic data. |
Keywords
* Artificial intelligence * Latent space * Likelihood
