The temporal logic of causal structures

Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

Volumn , Issue , 2009, Pages 303-312

  Kleinberg, Samantha ^a   Mishra, Bud ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; CURRICULA; DATA PRIVACY; ELECTRONIC TRADING; GENE EXPRESSION; MODEL CHECKING; NETWORK SECURITY; TEMPORAL LOGIC;

COMPUTATIONAL ANALYSIS; FALSE DISCOVERY CONTROLS; GENE EXPRESSION LEVELS; MINIMAL DESCRIPTION LENGTH; MULTIPLE HYPOTHESES TESTING; MULTIPLE HYPOTHESIS TESTING; STATISTICAL TECHNIQUES; STOCK PRICE MOVEMENTS;

COMPUTER CIRCUITS;

EID: 80053160910     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. R. Agrawal and R. Srikant. Mining sequential patterns. In Proc. of the Eleventh International Conference on Data Engineering, 1995.
2. C. Baier, E. Clarke, V. Hartonas-Garmhausen, et al. Symbolic model checking for probabilistic processes. In Proc. 24th International Colloquium on Automata, Languages and Programming, volume 1256 of LNCS. Springer, 1997.
3. Z. Bar-Joseph. Analyzing time series gene expression data. Bioinformatics, 20(16):2493-2503, 2004. (Pubitemid 39530134)
4. Z. Bozdech, M. Llinás, B. Pulliam, et al. The Transcriptome of the Intraerythrocytic Developmental Cycle of Plasmodium falciparum. PLoS Biol, 1(1): e5, 2003.
5. E. M. Clarke, O. Grumberg, and D. A. Peled. Model Checking. MIT Press, 1999.
6. E. Eells. Probabilistic Causality. Cambridge University Press, 1991.
7. B. Efron. Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis. Journal of the American Statistical Association, 99(465):96-105, 2004.
8. M. Eichler and V. Didelez. Causal reasoning in graphical time series models. In Proc. of the 23rd Conference on Uncertainty in Artificial Intelligence, 2007.
9. N. Friedman, K. Murphy, and S. Russell. Learning the structure of dynamic probabilistic networks. In Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, 1998.
10. N. Friedman, M. Linial, I. Nachman, et al. Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology, 7(3-4):601-620, 2000.
11. C. W. Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3):424-438, 1969.
12. H. Hansson and B. Jonsson. A logic for reasoning about time and reliability. Formal Aspects of Computing, 6(5):512-535, 1994.
13. J. Jin and T. T. Cai. Estimating the Null and the Proportion of non-Null effects in Large-scale Multiple Comparisons. Journal of the American Statistical Association, 102:495-506, 2006.
14. C. Langmead, S. Jha, and E. Clarke. Temporal logics as query languages for dynamic bayesian networks: Application to d. melanogaster embryo development. Technical Report CMU-CS-06-159, Carnegie Mellon University, 2006.
15. C. J. Langmead. Towards inference and learning in dynamic bayesian networks using generalized evidence. Technical Report CMU-CS-08-151, Carnegie Mellon University, 2008.
16. K. Murphy and S. Mian. Modelling gene expression data using dynamic bayesian networks. Technical report, University of California, Berkeley, CA, 1999.
17. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.
18. H. Reichenbach. The Direction of Time. University of California Press, 1956.
19. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, 2000.
20. P. Spirtes, C. Glymour, R. Scheines, et al. Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data. In Proc. of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology, 2001.
21. P. Suppes. A probabilistic theory of causality. NorthHolland, 1970.