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Found 5 papers, 2 papers with code
Learning Multi-Frequency Partial Correlation Graphs
Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands.
Extracting the Multiscale Causal Backbone of Brain Dynamics
The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing brain dynamics.
Learning Multiscale Non-stationary Causal Structures
Our model allows sampling an MN-DAG according to user-specified priors on the time-dependence and multiscale properties of the causal graph.
Multiscale Causal Structure Learning
The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system.
The Evolving Causal Structure of Equity Risk Factors
They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor.
