The EM algorithm for graphical association models with missing data

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, ...

Bayesian hierarchical models are probabilistic models that have hierarchical structures and use Bayesian methods for inferences. In this paper, we extend Fitts’ law to be a Bayesian hierarchical pointing model and compare it with the typical pooled ...

Graphical models are a powerful tool for describing patterns of conditional independence, and can also be used to regularize the covariance matrix. Vertices in the graph represent variables, and in the Gaussian setting, edges between vertices are ...