Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Детерминистичен Марков модел× | Дискретно-събитийна симулация (DES)× | |
|---|---|---|
| Област | Симулационно моделиране | Симулационно моделиране |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1993 | 1960s (formalized); modern computational form from 1970s onward |
| Създател≠ | Sonnenberg, F. A. & Beck, J. R. | Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s) |
| Тип≠ | Cohort state-transition model with fixed transition probabilities | Stochastic process simulation |
| Основополагащ източник≠ | Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: a practical guide. Medical Decision Making, 13(4), 322–338. DOI ↗ | Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127 |
| Други названия≠ | DMM, Deterministic Markov Chain, Cohort Markov Model, Fixed-Parameter Markov Model | DES, event-driven simulation, Ayrık Olay Simülasyonu (DES) |
| Свързани≠ | 5 | 4 |
| Резюме≠ | 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. | Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time. |
| ScholarGateНабор от данни ↗ |
|
|