Summary of Learning the Influence Graph Of a High-dimensional Markov Process with Memory, by Smita Bagewadi and Avhishek Chatterjee
Learning the Influence Graph of a High-Dimensional Markov Process with Memory
by Smita Bagewadi, Avhishek Chatterjee
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 learns the underlying causal graph or influence graph of a high-dimensional multivariate discrete-time Markov process with memory. The process involves binary strings of random length, parameterized by hidden scalars that evolve according to linear stochastic dynamics governed by the influence graph. An extension of an existing algorithm for i.i.d. graphical models is developed and shown to learn the influence graph using logarithmic samples when the degree of the graph is bounded. The sample complexity result is derived by upper and lower bounding the rate of convergence of the observed Markov process with memory to its stationary distribution in terms of the parameters of the influence graph. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way has been discovered to learn how different variables affect each other, which is important for many real-life applications. Imagine you’re trying to understand how people are influenced by each other’s opinions on social media, or how a person’s brain processes information from different parts. This method can help with that! It works by analyzing patterns in the way variables change over time, and it only needs a limited amount of data to get accurate results. |
