Permutation testing improves bayesian network learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6323 LNAI, Issue PART 3, 2010, Pages 322-337

  Tsamardinos, Ioannis ^a   Borboudakis, Giorgos ^a  

^a UNIVERSITY OF CRETE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC TESTS; BASIC PROCEDURE; BAYESIAN NETWORK LEARNING; CATEGORICAL DATA; CONDITIONAL INDEPENDENCES; CONSTRAINT-BASED; DATA SETS; DIFFERENT DISTRIBUTIONS; EXACT TESTS; GRAPHICAL MODEL; LEARNING BEHAVIOR; LEARNING NETWORK; LEARNING PROBLEM; META-ANALYSIS; MONTE CARLO; P-VALUES; PERMUTATION PROCEDURES; PERMUTATION TESTING; SEMIPARAMETRIC; SMALL SAMPLE SIZE; STRUCTURAL LEARNING; TYPE-I ERROR; VARIABLE SELECTION ALGORITHMS;

ASYMPTOTIC ANALYSIS; BAYESIAN NETWORKS; DISTRIBUTED PARAMETER NETWORKS; DISTRIBUTION FUNCTIONS; GRAPHIC METHODS; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING SYSTEMS; SPEECH RECOGNITION;

STATISTICAL TESTS;

EID: 77958036957     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15939-8_21     Document Type: Conference Paper

References (19)

1. Agresti, A.: Categorical Data Analysis, 2nd edn. Wiley Series in Probability and Statistics. Wiley-Interscience, Hoboken (2002)
2. Agresti, A.: A survey of exact inference for contingency tables. Statistical Sci-ence 7(1), 131-153 (1992)
3. Aliferis, C.F., et al.: Local causal and markov blanket induction for causal discovery and feature selection for classification part i: Algorithms and empirical evaluation. JMLR 11, 171-234 (2010)
4. Beinlich, I., Suermondt, G., Chavez, R., Cooper, G.: The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. Artificial Intelligence in Medicine, 247-256 (1989)
5. Frank, E., Witten, I.H.: Using a permutation test for attribute selection in decision trees. In: ICML, pp. 152-160. Morgan Kaufmann, San Francisco (1998)
6. Good, P.: Permutation, Parametric, and Bootstrap Tests of Hypotheses, 3rd edn. Springer Series in Statistics. Springer, Heidelberg (2004)
7. Hong, F., Breitling, R.: A comparison of meta-analysis methods for detecting dif-ferentially expressed genes in microarray experiments. Bioinformatics 24, 374-382 (2008)
8. Jensen, D.: Induction with Randomization Testing: Decision-Oriented Analysis of Large Data Sets. Ph.D. thesis, Washington University (1992)
9. Jensen, D., Neville, J.: Randomization tests for relational learning. Tech. Rep. 03-05, Department ofComputer Science, University ofMassachusetts Amherst (2003)
10. Mehta, C.P.: Statxact: A statistical package for exact nonparametric inference. The American Statistician 45, 74-75 (1991)
11. Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: 9th ACM SIGKDD (2003)
12. Richardson, T., Spirtes, P.: Ancestral graph markov models. Annals of Statis-tics 30(4), 962-1030 (2002)
13. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge (2000)
14. Tillman, R.E.: Structure learning with independent non-identically distributed data. In: 26th International Conference on Machine Learning, ICML 2009 (2009)
15. Tillman, R.E., Danks, D., Glymour, C.: Integrating locally learned causal struc-tures with overlapping variables. In: NIPS (2008)
16. Triantafyllou, S., Tsamardinos, I., Tollis, I.G.: Learning causal structure from over-lapping variable sets. In: AI and Statistics (2010)
17. Tsamardinos, I., Brown, L., Aliferis, C.: The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm. Machine Learning 65(1), 31-78 (2006)
18. Tsamardinos, I., Triantafyllou, S.: The possibility of integrative causal analysis: Learning from different datasets and studies. Journal of Engineering Intelligent Systems (to appear, 2010)
19. Tsamardinos, I., Brown, L.E.: Bounding the false discovery rate in local bayesian network learning. In: AAAI, pp. 1100-1105 (2008)