Summary of An Inferential Measure Of Dependence Between Two Systems Using Bayesian Model Comparison, by Guillaume Marrelec and Alain Giron
An inferential measure of dependence between two systems using Bayesian model comparison
by Guillaume Marrelec, Alain Giron
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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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 proposed method quantifies dependence between two systems X and Y based on the Bayesian comparison of statistical independence (H0) and dependence (H1) models. The dependence measure, denoted B(X,Y|D), is the posterior probability for H1 given the dataset D or any strictly increasing function thereof. This measure captures the evidence for dependence between X and Y as modeled by H1 and observed in D. The paper reviews various statistical models, reconsiders standard results, and derives general properties of B(X,Y|D). The method is evaluated using simulations, focusing on noise effects and the behavior of B(X,Y|D) under varying intensity of dependence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to measure how two things are connected. They use something called Bayesian statistics to compare two different models: one that says the connection is random, and another that says it’s real. The measure they come up with is like a score that shows how strong the connection is based on some data. They test this method using fake data and look at what happens when there’s noise or if the connection gets stronger or weaker. |
Keywords
» Artificial intelligence » Probability
