Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs

no code implementations • 6 Feb 2024 • He Zhao, Vassili Kitsios, Terence J. O'Kane, Edwin V. Bonilla

We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data. Vector autoregressive (VAR) models have been time-tested for this problem, including Bayesian variants and more recent developments using deep neural networks.

Time Series Uncertainty Quantification

Variational DAG Estimation via State Augmentation With Stochastic Permutations

no code implementations • 4 Feb 2024 • Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane

Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG), from observational data is a statistically and computationally hard problem with essential applications in areas such as causal discovery.

Causal Discovery Uncertainty Quantification +1