Learning markov models for stationary system behaviors
NFM'12 Proceedings of the 4th international conference on NASA Formal Methods
Learning stochastic timed automata from sample executions
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
Performance evaluation of sensor networks by statistical modeling and euclidean model checking
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
Continuous-time Markov Chains (CTMCs) are an important class of stochastic models that have been used to model and analyze a variety of practical systems. In this paper we present an algorithm to learn and synthesize a CTMC model from sample executions of a system. Apart from its theoretical interest, we expect our algorithm to be useful in verifying black-box probabilistic systems and in compositionally verifying stochastic components interacting with unknown environments. We have implemented the algorithm and found it to be effective in learning CTMCs underlying practical systems from sample runs.