Causality with gates

Journal of Machine Learning Research

Volumn 22, Issue , 2012, Pages 1314-1322

  Winn, John ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CALCULATIONS; GRAPHIC METHODS; INFERENCE ENGINES; MESSAGE PASSING; SPEECH RECOGNITION;

BELIEF PROPAGATION; CAUSAL REASONING; DO-CALCULUS; GRAPHICAL MODEL; GRAPHICAL MODELLING; INFERENCE ALGORITHM; PROBABILISTIC INFERENCE;

RECONFIGURABLE HARDWARE;

EID: 84954219675     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (15)

1. T. Minka and J. Winn. Gates. In Advances in Neural Information Processing Systems 21, 2009.
2. J. Pearl. Causal diagrams for empirical research. Biometrika, 82:669-688, 1995.
3. T. Minka and J. Winn. Gates: A graphical notation for mixture models. Technical Report MSRTR-2008-185, Microsoft Research Ltd, 2008.
4. B. Frey, F. Kschischang, H. Loeliger, and N. Wiberg. Factor graphs and algorithms. In Proc. of the 35th Allerton Conference on Communication, Control and Computing, 1998.
5. J. Pearl. Causality-models, reasoning, and inference. Cambridge University Press, second edition, 2000, 2009.
6. A. P. Dawid. Influence diagrams for causal modelling and inference. Intl. Stat. Review, 70:161-189, 2002. Corrections p437.
7. P. Spirtes, C. N. Glymour, and R. Scheines. Causation, Prediction and Search. Springer-Verlag, 1993.
8. J. Pearl. Comment: Graphical models, causality, and intervention. Statist. Sci., 8:266-9, 1993.
9. D. Geiger, T. Verma, and J. Pearl. Identifying independence in Bayesian networks. Networks, 20:507-534, 1990.
10. B. Frey. Extending factor graphs so as to unify directed and undirected graphical models. In Proc. of the 19th conference on Uncertainty in Artificial Intelligence, pages 257-264, 2003.
11. T. P. Minka. Expectation propagation for approximate Bayesian inference. In Proceedings of the 17th conference on Uncertainty in Artificial Intelligence, pages 362-369, 2001.
12. J. Winn and C. M. Bishop. Variational Message Passing. JMLR, 6:661-694, 2005.
13. D. Eaton and K. Murphy. Exact Bayesian structure learning from uncertain interventions. In Proc. of the 8th workshop on Artificial Intelligence and Statistics, 2007.
14. J. Tian and J. Pearl. Causal discovery from changes. In Proceedings of the 17th conference on Uncertainty in Artificial Intelligence, 2001.
15. A. P. Dawid. Causal inference using influence diagrams: the problem of partial compliance. In Highly structured stochastic systems, number 27 in Oxford Statistical Science Series. Oxford University Press, 2003.