LITree: LITree: A package for infering Gaussian Graphical Models with...

In cambroise/LITree: Latent Incomplete Tree Inference via Graphical Gaussian Model

Description

The LITree package provides four classes graphModel, GGMmodel, GGMfit, GGMexperiment and one function em.litree()

Details

The function em.litree implements the algorithm of Gaussian Graphical Model Inference with missing variable described in Robin et. al (2017). The underlying model is based on the aggregation of spanning trees, and the estimation procedure on the Expectation-Maximization algorithm. We treat the graph structure and the unobserved nodes as missing variables and compute posterior probabilities of edge appearance. To provide a complete methodology, we also propose three model selection criteria to estimate the number of missing nodes.

Author(s)

Genevi<c3><a8>ve Robin [e-mail]

Christophe Ambroise [e-mail]

St<c3><a9>phane Robin st<c3><a9>[e-mail]

References

Genevi<c3><a8>ve Robin, Christophe Ambroise, St<c3><a9>phane Robin (Submitted on 26 May 2017). Graphical model inference with unobserved variable via latent tree aggregation. Arxiv Paper. https://arxiv.org/abs/1705.09464

cambroise/LITree documentation built on May 6, 2019, 8:32 p.m.