Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beiešiskā jutīguma analīze× | Bayesiešu Markovas modelis× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1984–1994 | 1990s–2000s |
| Autors≠ | Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration) | Briggs, A.; Sculpher, M.; and broader Bayesian statistics community |
| Tips≠ | Uncertainty propagation and sensitivity quantification | Probabilistic state-transition simulation |
| Pirmavots≠ | Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. DOI ↗ | Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629 |
| Citi nosaukumi | BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis | Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty. | A Bayesian Markov model is a state-transition simulation method that combines Markov chain cohort modeling with Bayesian statistical inference. By placing prior distributions on transition probabilities and updating them with observed data, the approach propagates full parameter uncertainty through the simulation, yielding posterior distributions over outcomes such as costs, life-years, or quality-adjusted life-years rather than single-point estimates. |
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