Likelihood-based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants

Summary

This paper shows that full likelihood-based inference is feasible for exponential family graphical models, including partially observed ones with latent nodes, under a logarithmically sparse setting. The method uses the tractable independence model underlying an intractable graphical model to estimate the normalizing constant and its gradient. Numerical results, theory, and comparisons with pseudo likelihood-based approaches support its applicability.