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Model Acronym |
Model Name |
Model Description |
Citation |
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GSMP+SE |
Generalized Semi-Markov Process (with Simultaneous Events) |
A model of a stochastic process with countably many states in which state transitions are probabilistically triggered by event (or multiple events) occurrence and the time of event occurrence is specified by a stochastic clock structure associated with an event set. |
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Generalized Semi-Markov Process (without Simultaneous Events) |
Generalized Semi-Markov Process models (GSMP) without simultaneously occuring events. |
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Semi-Markov Process |
A GSMP-SE with only one type of event. Events do not compete for each other and a single stochastic clock measures interevent time. |
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| Queuing Network |
The state of a queuing network counts the occupancy of each place/node (service station) in the network. |
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Continuous Time Markov Chain |
A continuous-time model of sequences of random variables with "memoryless" property. Represented by states with probabilistic transition rates. Can be viewed as SMP with Poisson distribution imposed on event clock. |
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Discrete Time Markov Chain |
A discrete-time model of sequences of random variables with "memoryless" property. Represented by states with probabilistic transitions occurring at discrete time. Can be viewed as SMP with geometric distribution imposed on event clock. |
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Stochastic Timed Automata |
State automata consisting of states and events (together with probabilistic transition function) and equipped with a stochastic clock structure associated with an event set (i.e. there’s a stochastic distribution function for each event that produces the time of event occurrence). |
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Probabilistic Timed Automata |
STA with deterministic transition function. |
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Timed Automata |
STA with deterministic transition function and deterministic clock structure. Clock structure determines the time at which events occur. |
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State Automata |
An abstract model consisting of discrete states and rules defining transitions from state to state. (Think of a directed graph, where nodes represent states and arcs represent transition rules). Transitions are triggered by events that occur in some order, but no time of the event occurrence is specified (no clock structure). |
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Deterministic Finite Automata |
State Automata with finitely many states. |
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Generalized Stochastic Petri Net |
Marked Petri Nets with timed and immediate transitions. Timed transitions are probabilistic and determined by a stochastic clock structure. |
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SPN |
Stochastic Petri Net |
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Timed Petri Net |
GSPN with deterministic clock structure and transition function. |
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Petri Net |
An abstract model that can be illustrated by a bipartite directed graph, with one set of nodes representing places and another set of nodes representing transitions. Each place may have a number of tokens which can move to another place when the corresponding transition is fired. Each arrangement of tokens (called marking) determines a state. Thus the states are not explicitly defined in the model. |
(Peterson, 1977) [survey] |
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Bounded Petri Net |
Petri Nets in which the number of tokens in each place is bounded from above. |
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Free Choice Net |
Petri Nets in which transitions w/ input from multi-output place have no other input arcs. |
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Event Graph |
An event graph is a directed graph in which each node specifies an event and each directed edge indicates an event casuality relationship. |
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