Process / pipelineSimulation / optimization

Bayesian Scenario Analysis — Probabilistic weighting of future scenarios via Bayesian inference

Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures.

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Sources

  1. Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231. DOI: 10.1016/j.ssci.2012.06.005
  2. Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation. ISBN: 9780833032973

Related methods

ScholarGateBayesian Scenario Analysis (Bayesian Scenario Analysis — Probabilistic scenario weighting via Bayesian inference). Retrieved 2026-06-04 from https://scholargate.app/en/simulation/bayesian-scenario-analysis