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| Bayesiansk mikrosimulering× | Markovmodel× | |
|---|---|---|
| Fagområde | Simulering | Simulering |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1990s–2000s | 1906 |
| Ophavsperson≠ | Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers | Andrei Markov |
| Type≠ | Individual-level probabilistic simulation with Bayesian updating | Probabilistic state-transition model |
| Oprindelig kilde≠ | Williamson, P., Birkin, M., & Rees, P. H. (2000). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30(5), 785-816. DOI ↗ | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Aliasser | Bayesian micro-simulation, BMS, Bayesian individual-level simulation, Probabilistic microsimulation | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | Bayesian Microsimulation combines individual-level simulation of heterogeneous populations with Bayesian statistical inference. Each synthetic individual follows a probabilistic life path, while model parameters are governed by prior beliefs updated with observed data. This approach is widely used in health technology assessment, public policy costing, and demographic projection, where uncertainty in both model inputs and structural assumptions must be formally quantified and propagated through to output estimates. | 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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