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Reliability analysis is based on stochastic discrete event models like stochastic Petri nets.
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A difference between the Petri net model and the other two models lies in the fact that the former is deterministic (with a discrete event formulation) while the latter two are based on stochastic event rates (the ODE is, of course, in a deterministic formulation).
One possible approach is to use state space models which consist of a time-continuous model for the system states, e.g. based on Stochastic Differential Equations (SDEs), and a separate model for the time-discrete measurements.
In this study, a comprehensive hydrolysis model based on stochastic molecular modeling approch in which each hydrolysis event is translated into a discrete event is presented.
Thus, we propose a new decision-aiding method based on stochastic multi-criteria acceptability analysis (SMAA) of the regret aversion behavior of decision makers (DM) with discrete Z-numbers.
NORMA is based on stochastic gradient descent.
Our mechanism is based on stochastic datagram relocation.
The algorithm is developed based on stochastic finite-element method.
KF technique makes predictions based on stochastic and dynamic structures.
The solution method is based on stochastic successive linear programming.
Computational models, routinely based on stochastic optimization, support these decisions.
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