Supervisory control of a class of discrete event processes
SIAM Journal on Control and Optimization
On observability of discrete-event systems
Information Sciences: an International Journal - Robotics and Automation/Control Series
Decentralized supervisory control of discrete-event systems
Information Sciences: an International Journal - Robotics and Automation/Control Series
Decentralized supervisory control of discrete event systems with nonhomogeneous control structure
Information Sciences: an International Journal
Fault diagnosis in discrete time hybrid systems - A case study
Information Sciences: an International Journal
Minimization of states in automata theory based on finite lattice-ordered monoids
Information Sciences: an International Journal
Decision making in fuzzy discrete event systems
Information Sciences: an International Journal
Determinization of fuzzy automata with membership values in complete residuated lattices
Information Sciences: an International Journal
Diagnosability of fuzzy discrete event systems
Information Sciences: an International Journal
The relationship of controllability between classical and fuzzy discrete-event systems
Information Sciences: an International Journal
State estimation and detectability of probabilistic discrete event systems
Automatica (Journal of IFAC)
Measuring the incremental information value of documents
Information Sciences: an International Journal
A new algorithm for testing diagnosability of fuzzy discrete event systems
Information Sciences: an International Journal
From classic observability to a simple fuzzy observability for fuzzy discrete-event systems
Information Sciences: an International Journal
On modeling of fuzzy hybrid systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In order to find better strategies for tobacco control, it is often critical to know the transitional probabilities among various stages of tobacco use. Traditionally, such probabilities are estimated by analyzing data from longitudinal surveys that are often time-consuming and expensive to conduct. Since cross-sectional surveys are much easier to conduct, it will be much more practical and useful to estimate transitional probabilities from cross-sectional survey data if possible. However, no previous research has attempted to do this. In this paper, we propose a method to estimate transitional probabilities from cross-sectional survey data. The method is novel and is based on a discrete event system framework. In particular, we introduce state probabilities and transitional probabilities to conventional discrete event system models. We derive various equations that can be used to estimate the transitional probabilities. We test the method using cross-sectional data of the National Survey on Drug Use and Health. The estimated transitional probabilities can be used in predicting the future smoking behavior for decision-making, planning and evaluation of various tobacco control programs. The method also allows a sensitivity analysis that can be used to find the most effective way of tobacco control. Since there are much more cross-sectional survey data in existence than longitudinal ones, the impact of this new method is expected to be significant.