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A General Framework for Mixed Graphical Models
"Mixed Data" comprising a large number of heterogeneous variables (e. g. count, binary, continuous, skewed continuous, among other data types) are prevalent in varied areas such as genomics and proteomics, imaging genetics, national security, social networking, and Internet advertising.
On Poisson Graphical Models
Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables.
Conditional Random Fields via Univariate Exponential Families
We thus introduce a “novel subclass of CRFs”, derived by imposing node-wise conditional distributions of response variables conditioned on the rest of the responses and the covariates as arising from univariate exponential families.
On Graphical Models via Univariate Exponential Family Distributions
Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications.
Graphical Models via Generalized Linear Models
Our models allow one to estimate networks for a wide class of exponential distributions, such as the Poisson, negative binomial, and exponential, by fitting penalized GLMs to select the neighborhood for each node.
