Learning continuous time Markov chains from sample executions

Proceedings - First International Conference on the Quantitative Evaluation of Systems, QEST 2004

Volumn , Issue , 2004, Pages 146-155

  Sen, Koushik ^a   Viswanathan, Mahesh ^a   Agha, Gul ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PATTERN RECOGNITION; PETRI NETS; QUEUEING NETWORKS; RELIABILITY;

AUTOMATIC COMPOSITIONAL VERIFICATION; EXECUTION TRACES; FORMAL VERIFICATION; SYSTEM CODES;

MARKOV PROCESSES;

EID: 16244362355     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/qest.2004.1348029     Document Type: Conference Paper

References (24)

1. D. Angluin. Learning regular sets from queries and counterexamples. Infor. and Comp., 75(2):87-106, 1987.
2. C. Baier, B. Haverkort, H. Hermanns, and J. Katoen. Model checking continuous-time Markov chains by transient analysis. In Computer Aided Verification (CAV'00), volume 1855 of LNCS, pages 358-372. Springer, 2000.
3. C. Baier, J. Katoen, H. Hermanns, and B. Haverkort. Simulation for continuous-time Markov chains. In 13th International Conference on Concurrency Theory (CONCUR'02), volume 2421 of LNCS, pages 338-354. Springer, 2002.
4. G. Balbo, G. Conte, S. Donatelli, G. Franceschinis, and M. A. Marsan. Modelling with Generalized Stochastic Petri Nets. John Wiley & Sons, 1995.
5. L. Baum, T. Petrie, G. Soules, and N. Weiss. A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics, 41(1):164-171, 1970.
6. R. C. Carrasco and J. Oncina. Learning stochastic regular grammars by means of a state merging method. In International Colloquium Grammatical Inference and Applications (ICGI'94), volume 862 of LNCS. Springer, 1994.
7. J. M. Cobleigh, D. Giannakopoulou, and C. S. Pasareanu. Learning assumptions for compositional verification. In Tools and Algorithms for the Construction and Analysis of Systems (TACAS '03), volume 2619 of LNCS, pages 331-346.
8. E. M. Gold. Language identification in the limit. Information and Control, 10:447-474, 1967.
9. A. Groce, D. Peled, and M. Yannakakis. Adaptive model checking. In Tools and Algorithms for the Construction and Analysis of Systems (TACAS'02), volume 2280 of LNCS, pages 357-371, 2002.
10. B. Haverkort. Performance of Computer Communication Systems: A Model-Based Approach. Wiley, 1998.
11. H. Hermanns, U. Herzog, and J. Katoen. Process algebra for performance evaluation. Theoretical Computer Science, 274(1-2):43-87, 2002.
12. H. Hermanns, J. P. Katoen, J. Meyer-Kayser, and M. Siegle. A tool for model-checking Markov chains. Software Tools for Technology Transfer, 4(2):153-172, 2003.
13. H. Hermanns, J. Meyer-Kayser, and M. Siegle. Multi-terminal binary decision diagrams to represent and analyse continuous-time Markov chains. In Workshop on the Numerical Solution of Markov Chains (NSMC'99), 1999.
14. J. Hillston. A Compositional Approach to Performance Modelling. Cambridge University Press, 1996.
15. R. V. Hogg and A. T. Craig. Introduction to Mathematical Statistics. Macmillan, New York, NY, USA, 1978.
16. C. Kermorvant and P. Dupont. Stochastic grammatical inference with multinomial tests. In Grammatical Inference: Algorithms and Applications, volume 2484 of Lecture Notes in Artificial Intelligence. Springer, 2002.
17. M. Z. Kwiatkowska, G. Norman, and D. Parker. PRISM: Probabilistic symbolic model checker. In Computer Performance Evaluation, Modelling Techniques and Tools (TOOLS'02), volume 2324 of LNCS, pages 200-204.
18. W. Nelson. Applied Life Data Analysis. Wiley, 1982.
19. J. Oncina and P. Garcia. Inferring regular languages in polynomial update time. In Pattern Recognition and Image Analysis, volume 1 of Series in Machine Perception and Artificial Intelligence, pages 49-61. 1992.
20. D. Ron, Y. Singer, and N. Tishby. On the learnability and usage of acyclic probabilistic finite automata. Journal of Computer and System Sciences, 56(2):133-152, 1998.
21. K. Sen, M. Viswanathan, and G. Agha. Statistical model checking of black-box probabilistic systems. In 16th conference on Computer Aided Verification (CAV'04), LNCS (To Appear). Springer, July 2004.
22. W. J. Stewart. Introduction to the Numerical Solution of Markov Chains. Princeton, 1994.
23. W. Wei, B. Wang, and D. Towsley. Continuous-time hidden Markov models for network performance evaluation. Performance Evaluation, 49(1-4):129-146, September 2002.
24. H. L. S. Younes, M. Kwiatkowska, G. Norman, and D. Parker. Numerical vs. statistical probabilistic model checking: An empirical study. In Tools and Algorithms for the Construction and Analysis of Systems (TACAS'04), volume 2988 of LNCS. Springer, 2004.