LiMIAM and DirectLiMIAM enable causal discovery from observational data under mean-independent but dependent disturbances, outperforming LiNGAM in simulations and recovering plausible orderings in oil market data.
and Miller, Karla L
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Introduces MFM-Wishart for clustering covariance matrices, with posterior consistency theory, MCMC algorithm, simulations, and application to infant brain functional connectivity.
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Causal discovery under mean independence and linearity
LiMIAM and DirectLiMIAM enable causal discovery from observational data under mean-independent but dependent disturbances, outperforming LiNGAM in simulations and recovering plausible orderings in oil market data.
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Mixture-of-Finite-Mixtures Wishart Model for Clustering Covariance Matrices with an Application to Brain Functional Connectivity
Introduces MFM-Wishart for clustering covariance matrices, with posterior consistency theory, MCMC algorithm, simulations, and application to infant brain functional connectivity.