Determining the number of non-spurious arcs in a learned DAG model: Investigation of a Bayesian and a frequentist approach

Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Volumn , Issue , 2007, Pages 251-258

  Listgarten, Jennifer ^a   Heckerman, David ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION DOMAINS; BAYESIAN APPROACHES; COMPUTATIONAL BIOLOGY; FALSE DISCOVERY RATE; FREQUENTIST; GRAPHICAL MODEL; IMMUNE SYSTEMS; NON-PARAMETRIC; POSTERIOR DISTRIBUTIONS; RELATIONSHIPS BETWEEN ENTITIES; STRUCTURE-LEARNING; SYNTHETIC DATASETS;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; BIOINFORMATICS; GRAPHIC METHODS; MODEL STRUCTURES;

STRUCTURAL DESIGN;

EID: 80053182984     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. *3002-restricted human immunodeficiency virus-specific cytotoxic T-lymphocyte epitopes by Elispot and intracellular cytokine staining assays. J Virol, 75:1339-1347, 2001. (Pubitemid 32094692)
2. David Heckerman and Jack Breese. Causal independence for probability assessment and inference using Bayesian networks. IEEE, Systems, Man, and Cybernetics, 26:826-831, 1996. (Pubitemid 126782451)
3. Yoav Benjamini and Yosef Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57(1):289-300, 1995.
4. Nir Friedman and Daphne Koller. Being bayesian about network structure. a bayesian approach to structure discovery in bayesian networks. Machine Learning, 50(1-2):95-125, 2003.
5. Bradley Efron. Local false discovery rates. Technical Report 2005-20B/234, Department of Statistics, Stanford University, March 2005.
6. David Heckerman, Dan Geiger, and David Chickering. Learning bayesian networks: The combination of knowledge and statistical data. In Proceedings of the 10th Annual Conference on Uncertainty in Artificial Intelligence (UAI-94), pages 293-30, San Francisco, CA, 1994. Morgan Kaufmann.
7. I.A. Beinlich, HJ. Suermondt, R.M. Chavez, and G.F. Cooper. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proceedings of the Second European Conference on Artificial Intelligence in Medicine, London, pages 247-256. Springer Verlag, Berlin, August 1989.
8. D. Heckerman, D. Geiger, and D. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
9. J. D. Storey and R. Tibshirani. Statistical significance for genomewide studies. Proc Natl Acad Sci USA, 100(16):9440-9445, August 2003. (Pubitemid 37033947)
10. John D. Storey. A direct approach to false discovery rates. J.R. Statist. Soc. B, 64:479-498, 2002.
11. Nir Friedman, Moises Goldszmidt, and Abraham Wyner. On the application of the bootstrap for computing confidence measures on features of induced bayesian networks. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume VII, 1999.
12. Nir Friedman, Moises Goldszmidt, and Abraham Wyner. Data analysis with bayesian networks: A bootstrap approach. In Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 196-20, San Francisco, CA, 1999. Morgan Kaufmann.
13. J.M. Pena, J. Björkegren, and J. Tegnér. Learning and validating bayesian network models of genetic regulatory networks. Advances in Probabilistic Graphical Models, 213, 2007.
14. J. Schäfer and K. Strimmer. An empirical bayes approach to inferring large-scale gene association networks. Bioinformatics, 21(6):754-764, March 2005. (Pubitemid 40439411)
15. R. Jurgelenaite and T. Heskes. Symmetric causal independence models for classification. In The third European Workshop on Probabilistic Graphical Models, 2006.