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Entropic covariance models
Another approach considers linear restrictions on the matrix logarithm of the covariance matrix.
Robust estimation of tree structured models
Consider the problem of learning undirected graphical models on trees from corrupted data.
Learning partial correlation graphs and graphical models by covariance queries
We study the problem of recovering the structure underlying large Gaussian graphical models or, more generally, partial correlation graphs.
Latent tree models
Latent tree models are graphical models defined on trees, in which only a subset of variables is observed.
Marginal likelihood and model selection for Gaussian latent tree and forest models
We clarify how in this case real log-canonical thresholds can be computed using polyhedral geometry, and we show how to apply the general theory to the Laplace integrals associated with Gaussian latent tree and forest models.
