Salīdzināt metodes

	Stohastiskā rindošanas simulācija	Stohastiskais Markova modelis
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1953	1993
Autors	Kendall, D. G.	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Tips	Stochastic simulation — waiting-line system analysis	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Pirmavots	Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. The Annals of Mathematical Statistics, 24(3), 338–354.	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.
Citi nosaukumi	SQS, Probabilistic Queueing Simulation, Stochastic Queue Modeling, Random Queueing Simulation	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Saistītās	6	6
Kopsavilkums	Stochastic Queueing Simulation models waiting-line systems where arrival and service processes follow probability distributions rather than fixed rates. By simulating thousands of random events, it estimates performance measures — mean waiting time, queue length, server utilization — under realistic uncertainty, making it the standard tool for designing and evaluating service systems from hospitals to call centers.	A Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates.