The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous wo...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
In constrained clustering it is common to model the pairwise constraints as edges on the graph of observations. Using results from graph theory, we analyze such constraint graphs ...
We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian network models to a successful...
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...
In recent years, a number of algorithms have been developed for learning the structure of Bayesian networks from data. In this paper we apply some of these algorithms to a realist...
Xiaofeng Wu, Peter J. F. Lucas, Susan Kerr, Roelf ...