Stochastic independence, algebraic independence and abstract connectedness
Theoretical Computer Science
Graph classes: a survey
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Iterative conditional fitting for Gaussian ancestral graph models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Convolutional factor graphs as probabilistic models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors
The Journal of Machine Learning Research
Robust graphical modeling with t-distributions
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Hi-index | 0.00 |
In graphical modelling, a bi-directed graph encodes marginal independences among random variables that are identified with the vertices of the graph. We show how to transform a bi-directed graph into a maximal ancestral graph that (i) represents the same independence structure as the original bi-directed graph, and (ii) minimizes the number of arrowheads among all ancestral graphs satisfying (i). Here the number of arrowheads of an ancestral graph is the number of directed edges plus twice the number of bi-directed edges. In Gaussian models, this construction can be used for more efficient iterative maximization of the likelihood function and to determine when maximum likelihood estimates are equal to empirical counterparts.