Comparer des méthodes

	Analyse Bayésienne de Scénarios	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s	1906
Auteur d'origine	Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s)	Andrei Markov
Type	Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis	Probabilistic state-transition model
Source fondatrice	Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231.	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Alias	BSA, Bayesian scenario planning, probabilistic scenario analysis, Bayesian-weighted scenario analysis	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	5	5
Résumé	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.	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.