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| Bayesian Agent-Based Modeling× | Bayes'i Markovi mudel× | |
|---|---|---|
| Valdkond | Simulatsioon | Simulatsioon |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2000s–2010s | 1990s–2000s |
| Looja≠ | Sunnaker et al. / Grazzini & Richiardi (among key contributors) | Briggs, A.; Sculpher, M.; and broader Bayesian statistics community |
| Tüüp≠ | Simulation calibration and inference framework | Probabilistic state-transition simulation |
| Algallikas≠ | Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803. DOI ↗ | Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629 |
| Rööpnimetused | Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation | Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation |
| Seotud≠ | 5 | 4 |
| Kokkuvõte≠ | Bayesian Agent-Based Modeling integrates Bayesian statistical inference with agent-based simulation to calibrate model parameters and quantify uncertainty. Rather than fixing agent rules and parameters by assumption, this approach treats unknown parameters as probability distributions and updates them systematically against observed data, yielding a full posterior over plausible model configurations. | 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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