Probabilistic soft interventions in Conditional Gaussian networks

AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics

Volumn , Issue , 2005, Pages 214-221

  Markowetz, Florian ^a   Grossmann, Steffen ^a   Spang, Rainer ^a  

^a MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FRAMEWORKS; GAUSSIAN NETWORKS; TARGET STATE;

ARTIFICIAL INTELLIGENCE;

BAYESIAN NETWORKS;

EID: 84862634658     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, and P. Walter. Molecular Biology of the Cell. Garland Science, New York, 4 edition, 2002.
2. S. G. Bøttcher. Learning Bayesian Networks with Mixed Variables. PhD thesis, Aalborg University, Denmark, 2004.
3. G. F. Cooper. A Bayesian Method for Causal Modeling and Discovery Under Selection. In Proceedings of UAI 16, pages 98-106, 2000.
4. G. F. Cooper and E. Herskovits. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 9:309-347, 1992.
5. G. F. Cooper and C. Yoo. Causal discovery from a mixture of experimental and observational data. In Proceedings of UAI 15, pages 116-125, 1999.
6. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological sequence analysis. Cambridge University Press, 1998.
7. N. Friedman. Inferring Cellular Networks Using Probabilistic Graphical Models. Science, 303(5659):799-805, 2004.
8. D. Geiger and D. Heckerman. Learning Gaussian Networks. In Proceedings of UAI 10, pages 235-243, 1994.
9. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman and HallCRC, 1996.
10. D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Machine Learning, 20(3):197-243, Sep. 1995.
11. S. L. Lauritzen. Graphical Models. Clarendon Press, Oxford, 1996.
12. S. L. Lauritzen. Causal Inference from Graphical Models. In: O. E. Barndorff-Nielsen, D. R. Cox, and C. Klüppelberg (eds.) Complex Stochastic Systems. Chapman and Hall, London, 2000.
13. K. P. Murphy. Active Learning of Causal Bayes Net Structure, Tech. Rep., UC Berkeley, 2001.
14. J. Pearl. Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge, 2000.
15. D. Pe'er, A. Regev, G. Elidan, and N. Friedman. Inferring subnetworks from perturbed expression profiles. Bioinformatics, 17(90001):S215-S224, 2001.
16. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, Cambridge, MA, second edition, 2000.
17. H. Steck and T. S. Jaakkola. Unsupervised active learning in large domains. In Proceedings of UAI 18, pages 469-476, 2002.
18. J. Tian and J. Pearl. Causal discovery from changes: a Bayesian approach. In Proceedings of UAI 17, pages 512-521, 2001.
19. S. Tong and D. Koller. Active Learning for Structure in Bayesian Networks. In Proceedings of IJCAI 17, 2001.
20. T. S. Verma and J. Pearl. Equivalence and synthesis of causal models. In Proceedings of UAI 6, pages 255-268, 1990.
21. C. Yoo and G. F. Cooper. An evaluation of a system that recommends microarray experiments to perform to discover gene-regulation pathways. Journal Artificial Intelligence in Medicine, 2003.
22. C. Yoo, V. Thorsson, and G. F. Cooper. Discovery of causal relationships in a generegulation pathway from a mixture of experimental and oberservational DNA microarray data. In Proceedings of PSB 7, pages 498-509, 2002.