  We introduce a general framework for undirected graphical models. It
generalizes Gaussian graphical models to a wide range of continuous, discrete,
and combinations of different types of data. The models in the framework,
called exponential trace models, are amenable to estimation based on maximum
likelihood. We introduce a sampling-based approximation algorithm for computing
the maximum likelihood estimator, and we apply this pipeline to learn
simultaneous neural activities from spike data.
