Salīdzināt metodes

	Deterministisk Markovas modelis	Stohastiskais Markova modelis
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1993	1993
Autors	Sonnenberg, F. A. & Beck, J. R.	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Tips	Cohort state-transition model with fixed transition probabilities	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Pirmavots	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: a practical guide. Medical Decision Making, 13(4), 322–338.	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	DMM, Deterministic Markov Chain, Cohort Markov Model, Fixed-Parameter Markov Model	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Saistītās	5	6
Kopsavilkums	A Deterministic Markov Model is a cohort-level state-transition model in which all transition probabilities, state utilities, and costs are assigned single fixed values and the model is solved analytically in a single pass. Widely used in health technology assessment, policy analysis, and operations research, it traces a hypothetical cohort through mutually exclusive health or system states over discrete time cycles, accumulating expected outcomes such as quality-adjusted life years (QALYs) or costs.	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.