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Found 2 papers, 1 papers with code
Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs
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.
Free-Form Variational Inference for Gaussian Process State-Space Models
However, inference in GPSSMs is computationally and statistically challenging due to the large number of latent variables in the model and the strong temporal dependencies between them.
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