Learning graphical models from data

what is independent of what

Learning the graph structure, not just the clique potentials.

Much more work.

Learning these models turns out to need a conditional independence test, an awareness of multiple testing and graph thoery.

  • bnlearn learns belief networks
  • sparsebn: > A new R package for learning sparse Bayesian networks and other graphical >models from high-dimensional data via sparse regularization. Designed from >the ground up to handle: > > * Experimental data with interventions > * Mixed observational / experimental data > * High-dimensional data with p >> n > * Datasets with thousands of variables (tested up to p=8000) > * Continuous and discrete data > >The emphasis of this package is scalability and statistical consistency on >high-dimensional datasets. For more details on this package, including >worked examples and the methodological background, please see our new >preprint. > > Overview > > The main methods for learning graphical models are: > > * estimate.dag for directed acyclic graphs (Bayesian networks). > * estimate.precision for undirected graphs (Markov random fields). > * estimate.covariance for covariance matrices. > > Currently, estimation of precision and covariances matrices is limited to Gaussian data.
  • Nonparanormal skeptic (🏗)
  • skggm (python) does the Gaussian thing but also has a nice sparsification and good explanation.

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