State space exploration in Markov models
SIGMETRICS '92/PERFORMANCE '92 Proceedings of the 1992 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
MASCOTS '95 Proceedings of the 3rd International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Markov Models for Pattern Recognition: From Theory to Applications
Markov Models for Pattern Recognition: From Theory to Applications
Reconstructing model parameters in partially-observable discrete stochastic systems
ASMTA'11 Proceedings of the 18th international conference on Analytical and stochastic modeling techniques and applications
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Many complex technical systems today have some basic protocol capability, which is used for example to monitor the quality of production output or to keep track of oil pressure in a modern car. The recorded protocols are usually used to detect deviations from some predefined standards and issue warnings. However, the information in such a protocol is not sufficient to determine the source or cause of the problem, since only part of the system is being observed. In this paper we present an approach to reconstruct missing information in only partially-observable stochastic systems based only on recorded system output. The approach uses Hidden non-Markovian Models to model the partially-observable system and Proxel-based simulation to analyze the recorded system output. Experiments were conducted using a production line example. The result of the analysis is a set of possible system behaviors that could have caused the recorded protocol, including their probabilities. We will show that our approach is able to reconstruct the relevant information to determine the source of non-standard system behavior. The combination of Hidden non-Markovian Models and Proxel-based simulation holds the potential to reconstruct unobserved information from partial or even noisy output protocols of a system. It adds value to the information already recorded in many production systems today and opens new possibilities in the analysis of inherently only partially-observable systems.