A conditional independence algorithm for learning undirected graphical models

A conditional independence algorithm for learning undirected graphical models

When it comes to learning graphical models from data, approaches based on conditional independence tests are among the most popular methods. Since Bayesian networks dominate research in this field, these methods usually refer to directed graphs, and thus have to determine not only the set of edges, but also their direction. At least for a certain kind of possibilistic graphical models, however,...

Learning Undirected Graphical Models with Structure Penalty

In undirected graphical models, learning the graph structure and learning the functions that relate the predictive variables (features) to the responses given the structure are two topics that have been widely investigated in machine learning and statistics. Learning graphical models in two stages will have problems because graph structure may change after considering the features. The main con...

Conditional Independence in Directed Cyclic Graphical Models for Feedback

Undirected Graphical Models

Read: Chapters 5 and 6 of [CGH]. The first model for a joint probability distribution that we will consider is the undirected graph. We will undirected graphs using two different methods. First, we will show the relationship between undirected graphs and joint probability distributions. Second, we will show the relationship between undirected graphs, and an abstract independence model (called a...

Approximate Contrastive Free Energies for Learning in Undirected Graphical Models

We present a novel class of learning algorithms for undirected graphical models, based on the contrastive free energy (CF). In particular we study the naive mean field, TAP and Bethe approximations to the contrastive free energy. The main advantage of CFlearning is the fact that it eliminates the need to infer equilibrium statistics for which mean field type approximations are particularly unsu...