Process / pipelineSimulation / optimization
Markov Model — Probabilistic State-Transition Modeling
A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.
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Sources
- Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
- Markov chain. Wikipedia. link ↗
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Agent-based Markov modelBayesian Cellular AutomataBayesian Markov ModelBayesian MicrosimulationBayesian Queueing SimulationBayesian Scenario AnalysisBayesian Sensitivity AnalysisBayesian System DynamicsDeterministic Cellular AutomataDeterministic Dynamic ProgrammingDeterministic Markov ModelDeterministic MicrosimulationMulti-objective Markov ModelPolicy Scenario Dynamic ProgrammingPolicy Scenario Queueing SimulationQueueing SimulationRobust Markov ModelStochastic Cellular AutomataStochastic Discrete-Event SimulationStochastic Dynamic ProgrammingStochastic Markov ModelStochastic Queueing Simulation