A review of undirected and acyclic directed Gaussian Markov model selection and estimation
Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties. We review model selection and estimation in directed and undirected Markov models with Gaussian parametrization, emphasizing the main similarities and differences. These two model types are foundationally similar but not equivalent, as we highlight. We report existing results with a unified notation and terminology, taking into account literature from both the artificial intelligence and statistics research communities, which first developed these models. Finally, we point out the main active research areas and open problems now existing with regard to these traditional, albeit rich, Markov models.