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Summary of Bayesian Vector Autoregression with Factorised Granger-causal Graphs, by He Zhao and Vassili Kitsios and Terence J. O’kane and Edwin V. Bonilla


Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs

by He Zhao, Vassili Kitsios, Terence J. O’Kane, Edwin V. Bonilla

First submitted to arxiv on: 6 Feb 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Machine Learning (stat.ML)

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GrooveSquid.com Paper Summaries

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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 Bayesian VAR model uses a hierarchical factorised prior distribution over binary Granger causal graphs to automatically discover Granger causal relations from observational multivariate time-series data. This approach replaces the traditional use of sparsity-inducing penalties/priors or post-hoc thresholds to interpret VAR coefficients as Granger causal graphs. The authors develop an efficient algorithm to infer the posterior over binary Granger causal graphs and demonstrate its performance on synthetic, semi-synthetic, and climate data.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper finds a way to figure out which things are connected in time series data without needing a lot of training data. It’s like trying to find clues about what happened before in a big mess of numbers. The new method is better at being unsure when it should be and needs fewer settings to get good results.

Keywords

* Artificial intelligence  * Time series