Nonlinear Markov networks for continuous variables
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Convex Optimization
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Directed cyclic graphical representations of feedback models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Identifying independencies in causal graphs with feedback
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A discovery algorithm for directed cyclic graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A polynomial-time algorithm for deciding Markov equivalence of directed cyclic graphical models
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Extending factor graphs so as to unify directed and undirected graphical models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning linear cyclic causal models with latent variables
The Journal of Machine Learning Research
Cyclic causal models with discrete variables: Markov Chain equilibrium semantics and sample ordering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows cycles. In addition to discussing inference and sampling with this representation, we give an exponential family parametrization that allows parameter estimation to be stated as a convex optimization problem; we also give a convex relaxation of the task of simultaneous parameter and structure learning using group l1-regularization. The model is evaluated on simulated data and intracellular flow cytometry data.